
import pandas as pd
#import data set
#view data
diabetes = pd.read_csv(r'I:\diabetes\delete数据\train.csv')
diabetes.info()
print(diabetes.describe())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5641 entries, 0 to 5640
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    5641 non-null   int64  
 1   age                    5641 non-null   int64  
 2   AST                    4421 non-null   float64
 3   ALT                    4421 non-null   float64
 4   ALP                    4421 non-null   float64
 5   GGT                    4421 non-null   float64
 6   TP                     4421 non-null   float64
 7   Alb                    4421 non-null   float64
 8   GLB                    4421 non-null   float64
 9   A/G                    4421 non-null   float64
 10  TG                     4423 non-null   float64
 11  TCHO                   4423 non-null   float64
 12  HDL-C                  4423 non-null   float64
 13  LDL-C                  4423 non-null   float64
 14  Urea                   4263 non-null   float64
 15  Cre                    4263 non-null   float64
 16  UA                     4263 non-null   float64
 17  HBsAg                  1363 non-null   float64
 18  Second liver antibody  1363 non-null   float64
 19  HBeAg                  1363 non-null   float64
 20  HBeAb                  1363 non-null   float64
 21  HBcAb                  1363 non-null   float64
 22  WBC                    5625 non-null   float64
 23  RBC                    5625 non-null   float64
 24  HGB                    5625 non-null   float64
 25  PCV                    5625 non-null   float64
 26  MCV                    5625 non-null   float64
 27  MCH                    5625 non-null   float64
 28  MCHC                   5625 non-null   float64
 29  RDW                    5625 non-null   float64
 30  PLT                    5625 non-null   float64
 31  MPV                    5618 non-null   float64
 32  PDW                    5618 non-null   float64
 33  PCT                    5618 non-null   float64
 34  NEUT%                  5625 non-null   float64
 35  LY%                    5625 non-null   float64
 36  MONO%                  5625 non-null   float64
 37  EOS%                   5625 non-null   float64
 38  BAS%                   5625 non-null   float64
 39  GLU                    5641 non-null   float64
dtypes: float64(38), int64(2)
memory usage: 1.7 MB
               sex          age          AST         ALT          ALP  \
count  5641.000000  5641.000000  4421.000000  4421.00000  4421.000000   
mean      0.489984    45.581280    26.805892    27.65202    87.482278   
std       0.499944    12.962952    13.563003    22.61152    25.630897   
min       0.000000     3.000000    10.040000     0.12000    22.980000   
25%       0.000000    35.000000    20.250000    15.13000    70.370000   
50%       0.000000    45.000000    23.890000    21.48000    84.470000   
75%       1.000000    54.000000    29.270000    32.30000   100.210000   
max       1.000000    93.000000   434.950000   498.89000   374.320000   

               GGT           TP          Alb          GLB          A/G  ...  \
count  4421.000000  4421.000000  4421.000000  4421.000000  4421.000000  ...   
mean     38.823762    76.771108    45.822504    30.948604     1.502538  ...   
std      40.788282     4.016055     2.617401     3.565997     0.220621  ...   
min       6.360000    57.320000    29.540000     7.060000     0.520000  ...   
25%      17.810000    74.190000    44.130000    28.570000     1.360000  ...   
50%      26.190000    76.630000    45.820000    30.780000     1.490000  ...   
75%      43.850000    79.530000    47.570000    33.180000     1.630000  ...   
max     736.990000   100.410000    54.080000    66.180000     7.120000  ...   

               PLT          MPV          PDW          PCT        NEUT%  \
count  5625.000000  5618.000000  5618.000000  5618.000000  5625.000000   
mean    253.082667    10.653346    13.304254     0.267901    56.717404   
std      59.591793     0.985545     2.173877     0.060507     7.800225   
min      37.000000     7.100000     8.000000     0.042000    14.400000   
25%     213.000000    10.000000    11.700000     0.230000    51.600000   
50%     249.000000    10.600000    12.900000     0.260000    56.700000   
75%     289.000000    11.300000    14.600000     0.300000    62.000000   
max     745.000000    15.200000    25.300000     0.710000    88.500000   

               LY%        MONO%         EOS%         BAS%          GLU  
count  5625.000000  5625.000000  5625.000000  5625.000000  5641.000000  
mean     33.779218     6.856978     2.043982     0.603698     5.626111  
std       7.253566     1.567647     1.710997     0.291786     1.481999  
min       7.500000     3.100000     0.000000     0.000000     3.070000  
25%      28.800000     5.800000     0.900000     0.400000     4.920000  
50%      33.600000     6.700000     1.600000     0.600000     5.290000  
75%      38.500000     7.700000     2.600000     0.700000     5.760000  
max      76.300000    23.200000    22.500000     3.500000    23.620000  

[8 rows x 40 columns]


pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
x = diabetes.drop("GLU", axis = 1)
x


y = diabetes[["GLU"]]
y


# deletion of missing value
trains = diabetes.dropna(axis = 0,subset = ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea',
                                          'Cre','UA','HBsAg','Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV',
                                          'MCH','MCHC','RDW','PLT','MPV','PDW','PCT','NEUT%','LY%','MONO%','EOS%','BAS%','GLU'])
# sum after deletion
print(trains.isnull().sum())

sex                      0
age                      0
AST                      0
ALT                      0
ALP                      0
GGT                      0
TP                       0
Alb                      0
GLB                      0
A/G                      0
TG                       0
TCHO                     0
HDL-C                    0
LDL-C                    0
Urea                     0
Cre                      0
UA                       0
HBsAg                    0
Second liver antibody    0
HBeAg                    0
HBeAb                    0
HBcAb                    0
WBC                      0
RBC                      0
HGB                      0
PCV                      0
MCV                      0
MCH                      0
MCHC                     0
RDW                      0
PLT                      0
MPV                      0
PDW                      0
PCT                      0
NEUT%                    0
LY%                      0
MONO%                    0
EOS%                     0
BAS%                     0
GLU                      0
dtype: int64


trains.info()
print(len(trains))

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1354 entries, 2 to 5638
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    1354 non-null   int64  
 1   age                    1354 non-null   int64  
 2   AST                    1354 non-null   float64
 3   ALT                    1354 non-null   float64
 4   ALP                    1354 non-null   float64
 5   GGT                    1354 non-null   float64
 6   TP                     1354 non-null   float64
 7   Alb                    1354 non-null   float64
 8   GLB                    1354 non-null   float64
 9   A/G                    1354 non-null   float64
 10  TG                     1354 non-null   float64
 11  TCHO                   1354 non-null   float64
 12  HDL-C                  1354 non-null   float64
 13  LDL-C                  1354 non-null   float64
 14  Urea                   1354 non-null   float64
 15  Cre                    1354 non-null   float64
 16  UA                     1354 non-null   float64
 17  HBsAg                  1354 non-null   float64
 18  Second liver antibody  1354 non-null   float64
 19  HBeAg                  1354 non-null   float64
 20  HBeAb                  1354 non-null   float64
 21  HBcAb                  1354 non-null   float64
 22  WBC                    1354 non-null   float64
 23  RBC                    1354 non-null   float64
 24  HGB                    1354 non-null   float64
 25  PCV                    1354 non-null   float64
 26  MCV                    1354 non-null   float64
 27  MCH                    1354 non-null   float64
 28  MCHC                   1354 non-null   float64
 29  RDW                    1354 non-null   float64
 30  PLT                    1354 non-null   float64
 31  MPV                    1354 non-null   float64
 32  PDW                    1354 non-null   float64
 33  PCT                    1354 non-null   float64
 34  NEUT%                  1354 non-null   float64
 35  LY%                    1354 non-null   float64
 36  MONO%                  1354 non-null   float64
 37  EOS%                   1354 non-null   float64
 38  BAS%                   1354 non-null   float64
 39  GLU                    1354 non-null   float64
dtypes: float64(38), int64(2)
memory usage: 433.7 KB
1354


# pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
x_1 = trains.drop("GLU", axis = 1)
x_1


y_1 = trains[["GLU"]]
y_1


"""
feature engineering
from sklearn.preprocessing import StandardScaler

cols_x = ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',
          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',
          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%']
cols_y = ['GLU']
# 
ss = StandardScaler()
# 
x_1 = ss.fit_transform(x_1)
x_1 = pd.DataFrame(x_1, columns=cols_x)
# 
y_1 = ss.fit_transform(y_1)
y_1 = pd.DataFrame(y_1, columns=cols_y)
# 
trains_1 = pd.concat([x_1,y_1],axis=1)
trains_1
"""

"\nfrom sklearn.preprocessing import StandardScaler\n\ncols_x = ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',\n          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',\n          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%']\ncols_y = ['GLU']\n# 实例化\nss = StandardScaler()\n# 特征数据\nx_1 = ss.fit_transform(x_1)\nx_1 = pd.DataFrame(x_1, columns=cols_x)\n# 目标变量\ny_1 = ss.fit_transform(y_1)\ny_1 = pd.DataFrame(y_1, columns=cols_y)\n# 列拼接\ntrains_1 = pd.concat([x_1,y_1],axis=1)\ntrains_1\n"


# Histogram
import matplotlib.pyplot as plt
import seaborn as sns
import hvplot.pandas
%matplotlib inline

# Retina display
%config InlineBackend.figure_format = 'retina'

# plt.style.use('ggplot') 
plt.style.use("fivethirtyeight")

# Pandas Display three decimal 
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))

for k in ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',
          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',
          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%','GLU']:
    plt.figure(figsize = (15,5))
    sns.barplot(x = trains[k]. value_counts().index, y = trains[k].value_counts().values)
    plt.title(k)

C:\Users\Xzt\AppData\Local\Temp\ipykernel_60332\3297004865.py:19: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure(figsize = (15,5))


corr = trains.corr()
plt.figure(figsize = (25, 20))

sns.heatmap(corr,
           annot = True,
           fmt = ".1f",
#            cmap = "YlGn" 
           )
plt.show()


corr["GLU"].sort_values()

sex                     -0.141
RDW                     -0.087
LY%                     -0.061
PLT                     -0.059
Second liver antibody   -0.053
HDL-C                   -0.051
PCT                     -0.049
HBeAb                   -0.029
MONO%                   -0.008
HBeAg                    0.001
HBcAb                    0.001
BAS%                     0.004
GLB                      0.006
EOS%                     0.011
TP                       0.018
Alb                      0.019
MPV                      0.023
HBsAg                    0.028
PDW                      0.029
MCV                      0.045
UA                       0.048
Cre                      0.056
NEUT%                    0.056
A/G                      0.060
AST                      0.092
RBC                      0.097
ALT                      0.101
Urea                     0.106
MCH                      0.108
PCV                      0.123
GGT                      0.123
WBC                      0.140
MCHC                     0.148
HGB                      0.151
ALP                      0.154
LDL-C                    0.157
TCHO                     0.159
TG                       0.229
age                      0.320
GLU                      1.000
Name: GLU, dtype: float64


sns.pairplot(trains[["age","TG","TCHO","GLU"]]) # The first three characteristics of absolute value
plt.show()


# modelling
import sklearn
from sklearn.linear_model import LinearRegression
# Model instantiation
le = LinearRegression()
# process of fit
le.fit(x_1, y_1)
# acquire Regression coefficient
coef1 = le.coef_ #39 Regression coefficient
coef1

array([[-5.52156051e-01,  4.00228279e-02, -4.56247705e-03,
         6.68085391e-03,  1.81549885e-03,  1.29364456e-03,
         1.34291415e-02,  1.19946780e-02,  1.43446352e-03,
         3.02233134e-01,  9.79734772e-02,  1.25375496e-01,
        -2.21936162e-01, -7.42667536e-02,  2.27715980e-02,
        -1.49083694e-02, -1.67198074e-03,  7.44455951e-03,
         2.62223312e-03,  2.82771892e-02, -4.53599283e-02,
         6.39199033e-02,  9.12119916e-02,  7.70479336e-01,
        -4.01382933e-02,  3.75776442e+00,  6.71260408e-02,
        -1.55709660e-01,  3.64515847e-02, -1.09716031e-01,
        -9.92131312e-03, -2.21501802e-01,  3.32087046e-02,
         8.99753872e+00, -5.65457814e-01, -5.73000746e-01,
        -5.77836149e-01, -5.61206685e-01, -5.51808298e-01]])


# import test
testA = pd.read_csv(r'I:\diabetes\delete数据\testA.csv')
testA.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    1000 non-null   int64  
 1   age                    1000 non-null   int64  
 2   AST                    815 non-null    float64
 3   ALT                    815 non-null    float64
 4   ALP                    815 non-null    float64
 5   GGT                    815 non-null    float64
 6   TP                     815 non-null    float64
 7   Alb                    815 non-null    float64
 8   GLB                    815 non-null    float64
 9   A/G                    815 non-null    float64
 10  TG                     824 non-null    float64
 11  TCHO                   824 non-null    float64
 12  HDL-C                  824 non-null    float64
 13  LDL-C                  824 non-null    float64
 14  Urea                   806 non-null    float64
 15  Cre                    806 non-null    float64
 16  UA                     806 non-null    float64
 17  HBsAg                  169 non-null    float64
 18  Second liver antibody  169 non-null    float64
 19  HBeAg                  169 non-null    float64
 20  HBeAb                  169 non-null    float64
 21  HBcAb                  169 non-null    float64
 22  WBC                    995 non-null    float64
 23  RBC                    995 non-null    float64
 24  HGB                    995 non-null    float64
 25  PCV                    995 non-null    float64
 26  MCV                    995 non-null    float64
 27  MCH                    995 non-null    float64
 28  MCHC                   995 non-null    float64
 29  RDW                    995 non-null    float64
 30  PLT                    995 non-null    float64
 31  MPV                    994 non-null    float64
 32  PDW                    994 non-null    float64
 33  PCT                    994 non-null    float64
 34  NEUT%                  995 non-null    float64
 35  LY%                    995 non-null    float64
 36  MONO%                  995 non-null    float64
 37  EOS%                   995 non-null    float64
 38  BAS%                   995 non-null    float64
dtypes: float64(37), int64(2)
memory usage: 304.8 KB


# interpolation of test
test_del = testA.interpolate()
test_del = test_del.fillna(test_del.mean())
test_del


# prediction of test
predict_del = le.predict(test_del)
predict_del

array([[6.7031328 ],
       [6.30968515],
       [5.34669346],
       [6.12243261],
       [4.79086085],
       [5.2902784 ],
       [4.83877   ],
       [5.51205557],
       [6.18440629],
       [6.16274681],
       [4.89409414],
       [6.30422722],
       [6.24374531],
       [5.38902378],
       [5.55708083],
       [3.53166488],
       [5.35503955],
       [6.60596687],
       [4.72278844],
       [5.39137951],
       [5.63916729],
       [5.25779551],
       [6.56248884],
       [6.86251449],
       [5.76120729],
       [5.70041973],
       [5.19879157],
       [5.48700278],
       [5.83968266],
       [5.55275456],
       [5.14586145],
       [5.6722024 ],
       [5.68534531],
       [7.50101513],
       [6.01843649],
       [4.75027492],
       [5.62037325],
       [5.30907376],
       [5.6915954 ],
       [5.59321445],
       [5.86913663],
       [5.64164008],
       [5.81900801],
       [5.95268069],
       [6.05925672],
       [6.72988637],
       [6.11685979],
       [4.95709644],
       [4.54532436],
       [5.68622168],
       [4.74147731],
       [4.55744826],
       [4.83385781],
       [4.97767145],
       [4.88735661],
       [4.3984764 ],
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       [4.70714991],
       [4.41865417],
       [4.98467436],
       [4.56693116],
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       [4.78122305],
       [4.85665538],
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       [4.21808445],
       [5.81812491],
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       [5.73364886],
       [6.58870064],
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       [6.4149319 ],
       [6.3578201 ],
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       [6.16896247],
       [6.15940843],
       [6.28856073],
       [6.28167872],
       [5.49348852],
       [6.17134268],
       [6.26354114],
       [5.82663489],
       [6.74669746],
       [6.46650831],
       [5.49980712],
       [6.16410608],
       [6.38900265],
       [5.45075173],
       [5.83801977],
       [6.38121795],
       [5.93794035],
       [5.61972266],
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       [5.65339644],
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       [6.66940853],
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       [6.16207717],
       [5.71280086],
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       [5.16336377],
       [5.50349439],
       [6.11846747],
       [6.13904854],
       [5.23206198],
       [6.37413172],
       [5.11332317],
       [5.28096889],
       [6.21859256],
       [5.54424967],
       [5.91421301],
       [5.18661586],
       [5.00453692],
       [6.41639928],
       [5.41687847],
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       [6.39879619],
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# import test answer
test_answer = pd.read_csv(r'I:\diabetes\delete数据\answerA.csv')
test_answer.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   GLU     1000 non-null   float64
dtypes: float64(1)
memory usage: 7.9 KB


import numpy as np
from sklearn.metrics import mean_squared_error

# 得分
print("Score:", le.score(test_del, test_answer))
print("RSME:", np.sqrt(mean_squared_error(test_answer, predict_del)))

Score: 0.10856260563407061
RSME: 1.3670889466002125


from numpy import array

coef1

# results

le_df = pd.DataFrame()

le_df["name"] = test_del.columns.tolist()
le_df["coef"] = coef1.reshape(-1,1)

le_df


# compared answer and prediction
test_del = pd.DataFrame({"answer": test_answer["GLU"].tolist(),
                         "predict": predict_del.flatten()
                        })
test_del


# compared plot
test_del.plot(figsize=(18,10))
plt.show()


# answer > prediciton
len(test_del.query("answer > predict")) / len(test_del)

0.406


# evaluation of model
plt.scatter(test_answer, predict_del, label="test")
plt.plot([test_answer.min(), test_answer.max()],
         [test_answer.min(), test_answer.max()],
         'k--',
         lw=3,
         label="predict"
        )

plt.show()

	GLU
2	5.59
5	5.97
8	5.66
9	5.48
165	5.00
166	5.48
188	4.19
289	5.02
380	5.76
394	4.72
395	5.26
916	5.07
1116	7.75
1117	6.20
1118	5.72
1566	8.20
2242	4.78
2243	6.58
2244	5.13
2245	4.83
2246	5.17
2247	4.48
2248	4.41
2249	4.39
2250	5.74
2252	5.77
2253	6.01
2254	5.15
2255	6.24
2256	5.25
2257	5.17
2258	4.86
2259	4.93
2260	5.64
2266	4.63
2270	4.90
2271	5.24
2274	6.12
2277	5.78
2281	6.09
2284	4.74
2287	4.99
2289	4.90
2290	4.17
2291	4.46
2292	4.99
2293	4.75
2295	4.45
2296	4.40
2297	5.11
2298	6.14
2301	4.67
2306	5.45
2309	5.23
2310	11.68
2311	5.36
2312	11.13
2314	4.83
2315	4.82
2318	5.11
2319	5.02
2320	4.60
2335	5.21
2365	5.28
2421	7.91
2422	5.20
2423	5.77
2424	5.57
2425	4.97
2426	4.88
2428	5.22
2429	8.08
2430	5.45
2432	4.53
2433	7.16
2434	11.67
2435	4.79
2436	7.22
2437	5.41
2439	6.09
2441	5.46
2443	7.34
2451	4.55
2458	5.12
2463	5.80
2464	4.76
2466	6.18
2468	5.85
2470	5.41
2473	5.31
2477	5.61
2478	8.73
2490	6.01
2491	4.92
2493	5.90
2497	5.74
2536	4.64
2537	4.84
2538	4.31
2539	4.85
2541	4.72
2542	4.78
2543	5.25
2544	5.54
2549	6.67
2552	7.70
2556	5.07
2558	10.02
2559	5.47
2560	4.61
2562	5.76
2563	5.06
2566	5.02
2567	4.96
2568	5.30
2569	5.05
2577	7.19
2581	5.56
2582	4.78
2588	4.70
2591	5.89
2594	5.29
2596	4.68
2614	4.95
2615	4.94
2620	5.21
2621	5.49
2622	6.89
2623	4.81
2624	4.33
2635	5.05
2748	5.35
2857	4.98
2861	6.18
2862	5.88
2863	5.06
2864	5.80
2865	5.03
2866	4.99
2867	4.23
2868	6.06
2869	4.38
2870	4.85
2871	5.05
2872	4.65
2873	4.53
2874	4.87
2875	4.39
2876	4.88
2877	5.13
2878	4.92
2879	5.27
2880	3.77
2884	4.84
2885	4.47
2886	4.68
2887	10.15
2888	4.27
2890	4.68
2891	4.88
2892	5.22
2893	4.88
2894	4.78
2895	5.86
2896	4.97
2898	7.39
2900	4.95
2901	6.06
2902	6.68
2903	5.70
2904	4.58
2905	4.54
2906	4.76
2907	4.76
2908	5.12
2909	4.48
2911	4.40
2912	4.27
2915	4.96
2921	4.77
2922	5.32
2923	4.87
2925	6.72
2926	5.14
2927	6.08
2928	13.78
2930	5.10
2933	4.90
2934	4.96
2935	5.23
2936	4.79
2937	5.17
2938	5.33
2940	7.44
2943	4.82
2944	4.96
2945	4.76
2948	5.60
2949	4.91
2950	4.37
2951	5.08
2952	5.10
2953	5.48
2954	6.02
2955	4.83
2956	4.70
2957	5.02
2959	4.83
2960	4.88
2961	4.90
2964	4.88
2967	4.81
2975	4.66
2979	5.17
2983	5.05
2986	4.38
2987	4.50
2990	5.24
2991	6.35
2992	5.68
3165	4.38
3166	5.02
3167	4.94
3168	6.73
3169	4.19
3172	5.97
3173	6.14
3174	5.43
3175	4.57
3176	6.25
3177	9.22
3178	4.92
3179	5.58
3180	4.68
3181	6.36
3182	5.90
3183	5.38
3184	6.70
3185	5.99
3186	5.44
3187	5.34
3188	5.81
3189	6.28
3190	6.04
3191	6.02
3192	5.16
3193	5.91
3194	8.37
3195	5.65
3196	5.91
3197	9.23
3198	5.79
3199	4.39
3200	7.89
3201	6.35
3202	6.45
3203	6.22
3204	6.89
3205	5.44
3206	4.93
3207	5.52
3208	5.13
3209	5.74
3210	5.84
3211	5.24
3212	5.88
3213	5.24
3214	5.73
3215	6.06
3216	5.47
3217	7.55
3218	8.19
3219	10.62
3220	16.04
3221	5.44
3222	5.46
3223	5.26
3224	6.05
3225	5.44
3226	5.28
3227	6.38
3228	6.87
3229	4.55
3230	4.99
3231	5.59
3232	5.33
3233	4.75
3234	5.04
3235	5.93
3236	5.98
3237	3.93
3238	5.45
3239	4.98
3240	4.74
3241	4.17
3242	6.18
3243	6.34
3244	4.79
3245	5.71
3246	5.00
3247	5.22
3248	5.87
3249	5.27
3250	5.26
3251	5.17
3252	5.26
3253	4.97
3254	5.50
3255	5.02
3256	5.22
3257	4.66
3258	5.14
3259	5.01
3261	11.80
3262	15.30
3263	4.37
3264	5.73
3265	4.28
3266	5.04
3267	4.29
3268	4.80
3269	5.04
3270	5.56
3271	12.15
3274	4.76
3275	4.59
3276	4.17
3277	4.28
3278	4.25
3280	4.84
3283	5.41
3284	7.14
3286	4.70
3288	5.51
3289	5.03
3292	5.37
3293	4.89
3297	4.60
3299	4.83
3304	5.39
3308	6.04
3309	5.38
3310	5.18
3311	6.20
3314	6.01
3315	5.21
3316	5.54
3318	9.24
3319	6.02
3321	5.49
3325	4.79
3326	5.21
3329	4.93
3330	4.69
3331	4.54
3332	4.22
3337	4.76
3338	5.51
3342	5.38
3343	5.50
3344	5.56
3345	4.90
3348	7.18
3351	5.43
3353	5.67
3354	5.41
3355	8.76
3356	4.90
3358	6.16
3359	5.51
3360	5.62
3361	5.12
3363	5.10
3368	5.28
3369	4.97
3372	5.67
3374	5.37
3418	4.65
3419	6.41
3420	5.13
3421	12.00
3423	5.63
3425	5.03
3427	6.22
3428	4.57
3430	4.65
3431	5.34
3434	11.54
3436	4.66
3438	4.54
3439	5.31
3440	4.80
3441	4.49
3442	5.27
3443	8.41
3444	4.65
3445	4.86
3446	4.84
3447	4.70
3448	4.55
3449	5.13
3450	6.22
3454	5.01
3455	5.08
3456	5.66
3459	4.63
3460	4.91
3461	9.31
3463	4.67
3465	4.74
3466	5.16
3469	4.59
3470	5.68
3474	4.26
3477	4.96
3485	4.30
3486	5.00
3491	4.21
3496	5.50
3497	5.45
3499	4.61
3500	5.30
3501	5.52
3508	4.89
3513	4.87
3521	5.60
3522	7.00
3524	4.75
3527	5.53
3528	4.69
3529	5.61
3530	5.14
3535	4.71
3537	5.42
3538	5.28
3576	5.50
3581	5.27
3582	4.97
3583	5.06
3584	4.79
3585	5.66
3586	13.00
3587	4.76
3588	4.85
3589	5.44
3590	7.67
3598	8.23
3599	5.81
3600	6.20
3601	4.81
3603	6.84
3604	4.88
3605	5.08
3606	5.14
3607	5.45
3608	5.20
3611	9.40
3615	4.92
3616	4.29
3617	5.68
3621	5.89
3629	6.86
3632	5.44
3636	5.54
3637	5.29
3638	5.94
3639	4.99
3641	5.06
3642	5.35
3644	5.05
3646	5.45
3647	5.76
3652	5.64
3653	6.06
3654	5.48
3657	7.12
3658	5.49
3659	4.99
3660	5.86
3663	5.77
3664	5.30
3669	5.15
3672	4.94
3673	4.89
3674	5.70
3677	5.21
3680	5.03
3681	4.88
3682	7.29
3683	4.65
3684	6.14
3685	5.81
3686	5.29
3687	4.95
3688	5.59
3689	5.96
3690	6.24
3691	5.35
3692	4.92
3693	4.70
3694	5.54
3695	4.67
3696	4.64
3697	4.86
3698	5.07
4064	6.08
4065	5.97
4066	12.71
4067	5.42
4068	5.61
4069	6.16
4070	5.86
4072	5.42
4074	5.83
4076	5.07
4077	4.74
4078	5.48
4079	5.24
4081	5.74
4082	8.18
4083	5.36
4084	5.27
4085	5.44
4086	15.89
4087	8.62
4088	4.92
4089	5.37
4090	5.29
4091	4.71
4093	5.51
4094	5.31
4095	6.01
4097	5.40
4099	5.64
4100	5.90
4101	6.33
4102	4.91
4103	6.06
4104	4.66
4105	4.99
4106	6.67
4107	5.25
4108	5.40
4109	5.28
4110	5.26
4111	5.35
4116	4.81
4117	4.80
4118	5.00
4119	5.33
4120	4.55
4121	5.22
4122	4.24
4124	4.32
4125	6.16
4130	5.35
4131	16.37
4134	5.43
4135	4.38
4137	5.11
4141	5.87
4142	6.53
4143	6.53
4145	5.30
4147	4.73
4148	13.13
4149	7.67
4150	5.21
4155	6.21
4157	4.97
4158	7.93
4159	6.20
4161	4.74
4172	5.74
4176	5.77
4178	5.61
4181	4.90
4183	5.71
4187	5.52
4195	5.76
4196	4.32
4212	5.36
4214	5.51
4215	5.13
4216	5.70
4217	15.62
4218	4.89
4220	5.47
4222	6.36
4252	5.31
4253	4.49
4254	4.99
4255	5.51
4256	5.49
4258	6.97
4259	4.59
4260	4.64
4261	6.00
4262	5.58
4263	5.05
4264	4.97
4265	5.21
4266	4.60
4267	5.22
4268	5.03
4269	4.90
4271	4.59
4272	6.53
4273	5.20
4274	6.32
4275	4.49
4276	4.76
4277	5.11
4278	4.42
4279	5.13
4280	4.60
4281	5.75
4282	5.48
4283	4.75
4284	5.51
4285	5.73
4286	6.56
4287	4.92
4288	4.46
4289	4.87
4290	4.31
4291	4.88
4292	5.09
4293	5.21
4294	5.10
4295	5.12
4296	5.21
4297	5.26
4298	5.30
4299	6.00
4300	5.86
4301	4.57
4302	4.81
4303	6.08
4304	5.45
4305	4.92
4306	5.46
4307	6.21
4308	5.74
4309	5.35
4310	5.71
4311	5.44
4312	5.50
4313	4.84
4468	5.59
4469	5.65
4470	5.04
4471	6.22
4472	4.83
4473	12.40
4475	5.11
4476	7.21
4477	5.13
4478	4.97
4479	5.69
4481	8.39
4482	7.17
4485	4.67
4486	4.37
4490	5.10
4501	5.33
4504	10.51
4505	5.41
4512	5.05
4513	4.43
4515	5.33
4516	5.13
4517	4.92
4518	4.94
4519	4.32
4523	5.44
4524	4.85
4526	7.18
4527	5.15
4529	6.13
4531	5.03
4532	4.58
4539	5.16
4544	5.50
4545	5.25
4546	5.90
4547	5.61
4551	5.07
4552	5.65
4555	5.67
4566	6.78
4568	4.74
4569	4.07
4577	6.80
4578	5.94
4579	5.31
4580	5.77
4581	5.66
4582	6.33
4583	5.44
4584	5.58
4585	5.58
4586	8.47
4587	6.50
4588	7.78
4589	5.64
4590	12.90
4591	5.92
4592	6.47
4593	5.85
4594	4.85
4595	6.92
4596	6.42
4597	6.14
4598	7.03
4599	4.40
4600	7.40
4601	5.95
4602	7.07
4603	7.08
4604	14.26
4605	5.83
4606	5.73
4607	6.20
4608	6.65
4609	5.59
4610	6.35
4611	5.01
4612	9.12
4613	5.84
4614	5.41
4615	5.10
4616	6.61
4617	6.13
4618	5.75
4619	7.02
4620	8.88
4621	10.18
4622	5.86
4623	7.66
4624	5.94
4625	6.59
4626	11.15
4627	5.49
4628	5.12
4629	6.21
4630	8.62
4631	7.60
4632	6.34
4633	5.44
4634	13.57
4635	5.97
4636	5.63
4637	8.72
4638	4.66
4639	5.95
4640	5.67
4641	5.18
4642	6.29
4643	5.33
4644	8.83
4645	5.96
4646	6.70
4647	5.93
4648	6.38
4649	6.87
4650	6.35
4651	6.54
4652	5.47
4653	7.69
4654	5.73
4655	6.68
4656	5.40
4657	9.17
4658	5.51
4659	5.52
4660	9.78
4661	6.34
4662	6.28
4663	5.33
4664	8.35
4665	7.45
4666	6.96
4667	6.86
4668	6.99
4669	9.74
4670	5.84
4671	5.63
4672	6.73
4673	5.80
4674	6.55
4675	6.41
4676	6.21
4677	6.22
4678	9.73
4679	5.77
4680	15.70
4681	7.09
4682	5.93
4683	5.94
4684	5.55
4685	5.89
4686	5.71
4687	5.64
4688	10.43
4689	5.35
4690	5.52
4691	5.77
4692	5.82
4693	5.07
4694	5.47
4695	5.59
4696	5.20
4697	4.98
4698	6.26
4699	5.78
4700	9.03
4701	6.10
4702	5.54
4703	5.58
4704	7.61
4705	6.01
4706	5.61
4707	5.38
4708	6.29
4709	4.86
4710	6.08
4711	8.90
4712	4.88
4713	7.34
4714	5.50
4715	5.77
4716	5.88
4717	5.66
4718	5.62
4719	6.25
4720	6.18
4721	4.97
4722	6.82
4723	6.57
4724	7.32
4725	5.63
4726	5.88
4727	5.35
4728	5.87
4729	7.81
4730	5.47
4731	6.99
4732	5.04
4733	5.33
4734	4.65
4735	5.16
4736	5.66
4737	4.97
4738	5.18
4739	5.42
4740	5.50
4741	5.95
4742	6.16
4743	5.63
4744	6.03
4745	6.55
4746	5.44
4747	4.98
4748	5.26
4749	5.87
4750	7.05
4751	5.13
4752	5.68
4753	5.23
4754	5.70
4755	7.08
4756	8.48
4757	5.45
4758	5.28
4759	5.52
4760	4.36
4761	4.71
4762	6.09
4763	7.46
4764	5.18
4765	5.56
4766	7.98
4767	6.47
4768	5.78
4769	5.22
4770	5.92
4771	9.98
4772	6.56
4773	5.73
4774	5.78
4775	5.54
4776	5.25
4777	5.39
4778	5.40
4779	4.33
4780	5.75
4781	5.58
4782	5.52
4783	4.97
4784	5.36
4785	9.39
4786	5.40
4787	5.70
4788	6.14
4789	4.96
4790	11.91
4791	5.74
4792	5.67
4793	6.67
4794	4.52
4795	5.84
4796	7.69
4797	7.44
4798	4.88
4799	4.47
4800	6.36
4801	5.63
4802	4.48
4803	5.67
4804	6.13
4805	5.07
4806	7.25
4807	5.23
4808	5.30
4809	5.04
4810	5.31
4811	5.03
4812	10.34
4813	6.80
4814	5.35
4815	5.80
4816	7.53
4817	5.42
4818	6.14
4819	5.04
4820	6.19
4821	5.56
4822	7.98
4823	6.20
4824	6.47
4825	6.18
4826	5.63
4827	5.49
4828	6.06
4829	5.54
4830	5.96
4831	5.51
4832	11.82
4833	6.03
4834	5.42
4835	4.85
4836	6.38
4837	4.92
4838	4.74
4839	5.05
4840	5.50
4841	4.93
4842	6.76
4843	5.13
4844	5.97
4845	4.86
4846	5.47
4847	5.20
4848	4.98
4849	5.64
4850	5.91
4851	7.35
4852	4.97
4853	5.39
4854	8.28
4855	6.11
4856	5.24
4857	6.21
4858	5.51
4859	5.47
4860	5.61
4861	5.96
4862	4.82
4863	6.69
4864	6.10
4865	5.48
4866	5.38
4867	6.20
4868	5.53
4869	5.63
4870	5.11
4871	6.07
4872	5.73
4873	4.55
4874	5.71
4875	4.85
4876	5.54
4877	6.10
4878	5.79
4879	6.09
4880	5.63
4881	5.23
4882	5.86
4883	5.53
4884	5.83
4885	7.29
4886	4.39
4887	4.95
4888	5.21
4889	7.33
4890	4.93
4891	5.22
4892	5.54
4893	5.43
4894	5.24
4895	5.54
4896	5.48
4897	4.74
4898	5.14
4899	4.90
4900	5.92
4901	4.99
4902	5.41
4903	4.87
4904	6.16
4905	5.31
4906	5.07
4907	6.09
4908	6.05
4909	5.33
4910	5.96
4911	5.51
4912	13.02
4913	6.90
4914	6.62
4915	10.16
4916	5.77
4917	5.45
4918	5.19
4919	5.55
4920	5.23
4921	5.86
4922	5.10
4923	5.16
4924	4.54
4925	5.09
4926	5.98
4927	4.73
4928	8.56
4929	5.33
4930	5.28
4931	4.76
4932	4.91
4933	6.30
4934	5.57
4935	5.00
4936	5.10
4937	5.11
4938	6.24
4939	4.79
4940	5.05
4941	4.49
4942	5.43
4943	5.71
4944	5.37
4945	5.40
4946	4.84
4947	5.79
4948	5.13
4949	6.16
4950	5.02
4951	10.70
4952	5.69
4953	5.55
4954	5.59
4955	4.87
4956	5.11
4957	4.94
4958	5.11
4959	5.39
4960	5.60
4961	5.26
4962	5.78
4963	5.20
4964	5.68
4965	5.51
4966	5.59
4967	4.93
4968	4.82
4969	5.27
4970	5.17
4971	5.47
4972	5.25
4973	5.32
4974	6.43
4975	6.64
4977	4.77
4978	4.69
4979	5.77
4980	5.37
4981	5.19
4982	4.80
4983	5.15
4984	5.51
4985	5.10
4986	5.70
4987	5.07
4988	5.66
4989	6.26
4990	5.04
4991	5.80
4992	4.92
4994	5.50
4995	5.32
4996	5.78
4997	5.29
4998	5.32
4999	5.75
5000	6.00
5001	5.44
5002	5.07
5003	5.47
5004	4.57
5005	4.83
5006	4.35
5007	4.71
5008	5.35
5009	5.12
5010	5.60
5011	5.59
5012	4.99
5013	4.34
5014	5.57
5015	5.72
5016	5.50
5017	4.47
5018	4.60
5019	4.97
5020	5.67
5021	5.20
5022	5.39
5023	5.42
5024	4.95
5025	6.05
5026	6.16
5027	5.30
5028	4.43
5029	4.14
5030	5.85
5031	5.88
5032	4.83
5033	5.93
5034	5.12
5035	4.66
5036	4.59
5037	4.62
5038	6.83
5039	5.04
5040	4.13
5041	5.40
5042	5.22
5043	5.59
5044	5.01
5045	5.67
5046	5.43
5047	5.58
5048	5.16
5049	5.52
5050	4.82
5051	5.10
5052	5.20
5053	5.38
5054	4.79
5055	4.77
5056	5.72
5057	4.94
5059	5.46
5060	6.87
5062	3.98
5064	4.40
5065	6.36
5066	12.75
5067	6.06
5070	7.20
5071	4.18
5072	5.10
5073	5.24
5080	7.21
5085	5.30
5086	4.59
5093	5.02
5095	5.36
5096	5.52
5098	4.98
5099	4.58
5103	5.59
5104	5.31
5105	4.23
5106	5.25
5107	6.15
5108	5.46
5114	4.92
5115	5.26
5116	4.19
5125	4.32
5133	4.81
5134	4.91
5139	5.03
5144	9.11
5146	13.21
5147	5.14
5148	5.17
5149	4.34
5150	7.46
5151	4.76
5152	5.38
5153	4.69
5154	12.67
5155	5.15
5156	4.34
5157	4.80
5238	5.32
5240	5.37
5241	5.38
5244	5.05
5245	5.81
5247	3.66
5248	5.39
5250	6.65
5251	5.54
5254	4.99
5255	5.36
5257	5.31
5266	5.53
5271	7.03
5272	6.08
5276	5.71
5277	5.66
5278	6.82
5279	5.61
5280	5.45
5282	9.63
5288	5.13
5290	5.14
5291	11.01
5292	5.33
5293	5.10
5295	5.83
5301	5.13
5304	5.15
5309	5.26
5310	5.50
5312	4.76
5321	5.26
5322	5.47
5323	5.21
5324	5.77
5327	9.48
5328	5.72
5329	5.89
5330	5.32
5331	5.56
5333	6.24
5335	6.13
5336	5.68
5337	5.34
5341	4.96
5342	5.20
5346	5.29
5347	5.71
5348	5.28
5349	7.36
5351	5.98
5353	5.33
5354	6.85
5355	9.09
5356	8.91
5357	6.23
5358	6.25
5359	12.14
5360	5.12
5361	6.30
5362	7.02
5363	5.88
5364	8.54
5365	5.38
5366	5.96
5367	5.77
5372	5.63
5373	5.55
5374	4.80
5375	5.65
5376	5.44
5377	4.92
5378	5.02
5382	4.84
5383	4.22
5384	4.86
5385	4.64
5386	5.37
5387	5.24
5388	4.96
5394	5.30
5397	4.91
5398	5.44
5402	4.98
5405	4.70
5406	5.04
5407	5.59
5408	4.68
5410	5.67
5412	5.10
5413	6.68
5420	5.39
5421	4.42
5422	5.30
5425	5.28
5427	4.77
5428	4.44
5429	4.54
5435	5.36
5437	5.23
5438	5.04
5439	5.38
5442	6.28
5443	5.48
5444	5.65
5454	5.66
5455	6.15
5462	9.60
5464	7.98
5469	6.22
5486	6.09
5518	5.08
5519	5.18
5574	6.65
5575	6.25
5576	4.44
5577	5.66
5580	5.82
5581	4.91
5582	5.52
5583	5.00
5587	5.77
5588	5.56
5590	4.77
5595	5.08
5601	5.59
5605	7.11
5606	6.56
5608	5.70
5609	11.04
5611	5.32
5615	4.79
5625	5.39
5632	5.25
5633	5.15
5638	5.24

	name	coef
0	sex	-0.552
1	age	0.040
2	AST	-0.005
3	ALT	0.007
4	ALP	0.002
5	GGT	0.001
6	TP	0.013
7	Alb	0.012
8	GLB	0.001
9	A/G	0.302
10	TG	0.098
11	TCHO	0.125
12	HDL-C	-0.222
13	LDL-C	-0.074
14	Urea	0.023
15	Cre	-0.015
16	UA	-0.002
17	HBsAg	0.007
18	Second liver antibody	0.003
19	HBeAg	0.028
20	HBeAb	-0.045
21	HBcAb	0.064
22	WBC	0.091
23	RBC	0.770
24	HGB	-0.040
25	PCV	3.758
26	MCV	0.067
27	MCH	-0.156
28	MCHC	0.036
29	RDW	-0.110
30	PLT	-0.010
31	MPV	-0.222
32	PDW	0.033
33	PCT	8.998
34	NEUT%	-0.565
35	LY%	-0.573
36	MONO%	-0.578
37	EOS%	-0.561
38	BAS%	-0.552

	answer	predict
0	5.500	6.703
1	5.310	6.310
2	5.050	5.347
3	7.230	6.122
4	5.930	4.791
5	5.530	5.290
6	4.570	4.839
7	5.990	5.512
8	5.500	6.184
9	6.250	6.163
10	5.000	4.894
11	5.470	6.304
12	5.120	6.244
13	5.350	5.389
14	7.320	5.557
15	5.610	3.532
16	5.040	5.355
17	6.400	6.606
18	5.600	4.723
19	5.610	5.391
20	5.580	5.639
21	6.110	5.258
22	5.800	6.562
23	6.330	6.863
24	5.480	5.761
25	4.670	5.700
26	5.570	5.199
27	5.770	5.487
28	5.860	5.840
29	5.520	5.553
30	5.060	5.146
31	4.860	5.672
32	5.330	5.685
33	5.810	7.501
34	5.140	6.018
35	5.910	4.750
36	5.650	5.620
37	5.100	5.309
38	4.710	5.692
39	5.580	5.593
40	4.520	5.869
41	7.070	5.642
42	5.430	5.819
43	5.680	5.953
44	5.590	6.059
45	5.690	6.730
46	14.040	6.117
47	5.300	4.957
48	5.030	4.545
49	6.330	5.686
50	5.160	4.741
51	6.600	4.557
52	4.810	4.834
53	5.550	4.978
54	4.810	4.887
55	4.390	4.398
56	4.610	5.017
57	5.130	4.707
58	5.350	4.419
59	4.810	4.985
60	5.360	4.567
61	5.030	5.416
62	6.340	7.060
63	5.400	4.820
64	4.720	4.781
65	5.400	4.857
66	5.310	4.659
67	5.130	4.218
68	5.520	5.818
69	4.910	6.122
70	9.760	6.512
71	5.690	5.959
72	4.400	6.405
73	4.510	5.348
74	5.200	5.878
75	4.560	5.024
76	5.210	5.612
77	5.570	5.734
78	6.880	6.589
79	5.670	6.465
80	5.680	6.415
81	5.020	6.358
82	6.210	5.065
83	4.980	6.169
84	5.570	6.159
85	5.160	6.289
86	5.230	6.282
87	5.430	5.493
88	5.710	6.171
89	5.770	6.264
90	4.950	5.827
91	6.250	6.747
92	6.720	6.467
93	8.410	5.500
94	5.470	6.164
95	5.300	6.389
96	5.980	5.451
97	6.020	5.838
98	6.460	6.381
99	6.630	5.938
100	5.550	5.620
101	5.900	5.450
102	10.160	6.698
103	8.610	5.653
104	9.100	6.261
105	4.920	5.899
106	5.550	5.447
107	4.640	5.424
108	4.800	6.399
109	6.240	6.669
110	5.960	6.440
111	5.130	6.161
112	5.980	6.162
113	6.140	5.713
114	5.210	6.557
115	6.020	5.163
116	5.390	5.503
117	5.080	6.118
118	5.840	6.139
119	4.660	5.232
120	6.010	6.374
121	4.870	5.113
122	5.290	5.281
123	5.550	6.219
124	5.130	5.544
125	6.380	5.914
126	5.130	5.187
127	4.330	5.005
128	5.400	6.416
129	4.640	5.417
130	5.260	6.332
131	5.250	6.255
132	5.110	6.257
133	5.820	5.999
134	5.170	5.262
135	5.420	6.399
136	4.870	5.301
137	6.120	5.564
138	6.110	6.045
139	6.950	5.403
140	5.340	5.326
141	5.350	5.818
142	6.250	6.034
143	6.630	5.769
144	5.300	6.194
145	4.640	4.706
146	5.920	6.636
147	4.760	5.048
148	5.790	5.344
149	4.370	4.370
150	7.160	4.630
151	5.010	5.623
152	5.300	6.157
153	5.260	6.574
154	5.460	4.670
155	4.850	6.202
156	4.990	5.877
157	7.270	6.566
158	5.700	5.824
159	4.740	5.905
160	5.030	5.208
161	6.340	5.968
162	5.720	6.802
163	5.500	5.187
164	5.600	5.020
165	6.110	6.099
166	4.920	5.524
167	5.240	5.825
168	4.760	6.323
169	5.410	6.571
170	6.160	5.853
171	5.300	5.069
172	5.390	6.039
173	6.090	7.065
174	5.560	4.566
175	5.630	5.929
176	4.890	5.387
177	5.490	6.347
178	4.670	6.455
179	5.000	5.955
180	5.980	6.092
181	5.490	4.973
182	5.010	5.282
183	5.220	5.527
184	5.510	6.775
185	5.290	5.511
186	5.120	4.873
187	6.270	6.019
188	5.360	5.721
189	4.970	4.311
190	4.630	5.484
191	5.220	5.210
192	7.780	6.302
193	5.280	6.385
194	5.160	5.854
195	5.370	5.539
196	5.270	5.337
197	6.410	6.326
198	5.630	4.919
199	10.020	6.281
200	5.150	5.333
201	5.690	3.608
202	5.170	4.670
203	7.280	6.174
204	5.080	5.128
205	5.510	5.541
206	5.890	6.278
207	6.210	7.016
208	6.440	6.677
209	5.130	4.557
210	5.220	5.355
211	4.870	6.860
212	4.790	6.100
213	5.000	4.797
214	5.980	6.077
215	5.330	6.210
216	6.170	6.382
217	5.750	6.042
218	8.210	6.101
219	5.390	6.968
220	7.300	6.544
221	4.720	5.312
222	4.460	5.998
223	4.420	5.794
224	5.560	5.852
225	5.730	4.368
226	4.780	5.676
227	6.000	5.988
228	5.670	5.003
229	4.740	5.197
230	5.540	4.101
231	4.660	4.498
232	4.870	4.378
233	5.820	6.136
234	5.020	4.562
235	6.810	6.203
236	4.970	6.680
237	5.870	6.064
238	5.360	5.874
239	5.250	6.066
240	6.170	6.249
241	7.750	6.474
242	5.350	6.536
243	5.600	4.827
244	6.220	6.119
245	10.630	6.768
246	6.200	6.137
247	5.680	7.529
248	5.250	5.188
249	6.930	6.578
250	6.350	5.466
251	4.910	5.263
252	6.710	6.368
253	5.930	6.855
254	7.280	6.273
255	5.150	5.369
256	5.130	5.338
257	5.820	5.834
258	9.540	5.781
259	4.780	4.917
260	5.750	5.348
261	5.420	5.269
262	6.290	6.079
263	5.100	4.962
264	5.280	7.058
265	5.820	6.517
266	6.980	6.279
267	14.530	6.737
268	5.720	6.529
269	5.120	5.443
270	5.250	4.967
271	5.980	5.409
272	6.570	6.478
273	4.840	5.271
274	5.520	6.146
275	6.070	5.383
276	7.330	6.542
277	5.500	5.985
278	5.550	5.650
279	5.260	4.285
280	4.940	5.651
281	6.220	5.804
282	5.720	5.730
283	9.320	6.038
284	6.110	5.899
285	5.500	5.590
286	6.010	6.472
287	5.060	6.027
288	5.440	5.414
289	5.580	5.943
290	6.210	5.754
291	6.000	5.818
292	8.440	6.924
293	13.240	6.338
294	5.680	4.474
295	5.540	4.843
296	7.100	5.696
297	5.110	5.500
298	6.000	5.625
299	6.410	5.344
300	7.900	5.888
301	5.190	4.859
302	5.980	5.829
303	9.090	6.502
304	5.480	5.333
305	5.340	5.924
306	5.180	7.417
307	5.250	5.343
308	5.880	6.284
309	5.280	5.484
310	5.600	6.518
311	4.540	6.709
312	4.860	4.738
313	15.240	8.087
314	5.200	4.835
315	5.600	5.462
316	6.070	4.817
317	6.660	5.196
318	9.360	6.394
319	5.210	6.396
320	4.810	6.114
321	4.820	5.409
322	4.730	4.419
323	5.330	5.354
324	5.420	6.278
325	5.660	6.127
326	5.300	6.487
327	5.930	5.093
328	5.500	5.544
329	5.550	6.325
330	5.410	7.081
331	5.130	6.569
332	5.690	5.782
333	5.540	6.099
334	5.340	6.052
335	5.950	5.438
336	5.670	6.171
337	5.600	6.282
338	5.440	5.658
339	7.740	7.193
340	5.490	5.968
341	5.860	5.744
342	6.100	4.595
343	5.560	5.388
344	5.620	5.505
345	5.390	5.559
346	4.910	5.548
347	5.420	5.653
348	5.530	4.909
349	5.330	6.268
350	5.230	4.501
351	4.860	4.803
352	5.340	5.819
353	6.730	5.767
354	4.910	5.634
355	4.980	6.034
356	6.810	5.247
357	5.010	5.171
358	5.840	4.710
359	5.920	5.454
360	5.260	5.741
361	5.820	6.467
362	4.990	5.162
363	5.350	5.510
364	5.560	6.927
365	5.340	6.067
366	6.250	6.139
367	5.250	5.101
368	6.950	6.665
369	5.490	6.226
370	9.030	5.974
371	5.290	6.320
372	6.990	6.695
373	4.950	5.101
374	4.570	7.002
375	4.550	4.813
376	5.680	5.845
377	4.990	5.876
378	5.910	6.242
379	5.280	5.869
380	5.020	5.019
381	5.740	5.384
382	4.940	5.291
383	7.780	6.222
384	5.640	5.442
385	5.040	6.153
386	6.500	6.401
387	5.880	6.984
388	5.510	5.161
389	4.980	4.695
390	5.370	6.079
391	5.850	6.228
392	5.950	6.259
393	5.200	4.550
394	5.390	6.723
395	6.570	5.959
396	7.050	6.177
397	5.800	5.244
398	5.880	6.837
399	5.130	5.030
400	5.240	5.304
401	4.930	5.583
402	5.440	5.363
403	8.790	7.090
404	4.840	4.940
405	5.360	6.214
406	5.100	5.609
407	4.830	5.846
408	10.680	6.841
409	5.020	5.747
410	5.840	6.487
411	3.850	4.967
412	5.050	5.775
413	6.070	6.834
414	5.400	5.423
415	5.710	5.977
416	4.950	4.721
417	5.130	5.978
418	5.400	5.671
419	5.600	5.894
420	6.240	5.735
421	6.370	5.447
422	6.560	6.487
423	4.980	5.369
424	5.000	5.085
425	5.130	5.390
426	5.350	4.965
427	5.150	5.240
428	4.580	4.822
429	5.990	5.530
430	4.980	4.107
431	5.670	4.773
432	5.150	6.146
433	5.280	6.159
434	5.490	7.086
435	7.880	6.342
436	12.790	6.551
437	5.020	5.274
438	5.060	6.425
439	4.750	5.749
440	5.640	5.779
441	4.930	6.299
442	5.980	6.487
443	5.120	6.172
444	4.920	6.497
445	5.380	6.244
446	9.680	6.535
447	5.430	6.464
448	5.130	5.811
449	5.540	5.927
450	5.360	6.321
451	13.840	6.771
452	5.820	5.854
453	4.980	6.679
454	7.440	5.832
455	5.740	6.733
456	6.490	6.071
457	4.580	5.634
458	5.930	6.198
459	10.420	6.746
460	14.900	6.670
461	5.150	6.110
462	4.910	5.593
463	5.100	5.592
464	7.330	6.112
465	4.930	6.193
466	5.810	6.499
467	4.960	5.469
468	5.260	5.961
469	5.510	5.737
470	5.220	5.758
471	5.300	5.437
472	4.720	5.637
473	5.340	5.669
474	5.230	5.950
475	5.480	5.582
476	5.660	5.586
477	5.230	5.367
478	5.140	5.556
479	4.700	4.538
480	6.200	5.629
481	5.110	5.801
482	6.840	4.632
483	5.280	5.039
484	5.150	5.380
485	6.030	5.371
486	4.470	5.372
487	5.460	5.358
488	4.540	5.283
489	5.090	5.396
490	4.380	5.317
491	5.160	4.960
492	4.060	5.484
493	5.560	5.370
494	5.610	5.441
495	5.240	3.955
496	4.670	4.962
497	5.570	5.089
498	4.410	4.846
499	5.150	4.680
500	4.710	5.144
501	5.410	4.440
502	5.030	5.156
503	11.790	5.570
504	4.930	5.115
505	5.800	5.196
506	5.160	5.227
507	5.480	4.527
508	4.850	5.167
509	5.310	4.878
510	5.680	5.546
511	6.020	5.584
512	5.050	5.886
513	5.170	5.618
514	4.990	5.716
515	5.030	5.452
516	4.970	4.907
517	6.040	6.336
518	5.020	5.103
519	5.350	4.842
520	5.050	5.335
521	5.190	4.747
522	6.030	5.326
523	5.720	5.152
524	7.020	5.320
525	5.080	4.869
526	5.580	5.013
527	4.760	5.226
528	5.120	4.430
529	4.530	4.837
530	4.890	4.814
531	4.870	4.817
532	5.160	5.097
533	4.600	4.797
534	7.720	5.251
535	5.300	5.190
536	5.200	4.608
537	4.850	4.725
538	5.170	4.128
539	5.010	4.482
540	5.400	4.934
541	4.620	4.995
542	6.370	6.006
543	4.910	4.990
544	13.100	6.346
545	7.500	5.982
546	4.500	5.218
547	5.830	5.976
548	5.040	5.115
549	5.950	6.258
550	5.690	5.583
551	5.540	6.613
552	5.320	5.820
553	5.470	5.155
554	5.090	5.602
555	5.180	6.155
556	5.870	5.611
557	5.540	6.022
558	5.600	5.483
559	4.810	4.439
560	5.680	5.811
561	5.820	6.571
562	5.320	5.802
563	5.590	5.523
564	12.890	6.590
565	7.550	6.416
566	4.730	4.959
567	9.340	5.844
568	5.710	6.015
569	11.300	6.996
570	4.850	4.744
571	6.300	6.417
572	5.170	5.853
573	4.710	4.561
574	5.750	5.070
575	4.890	4.472
576	5.970	6.207
577	5.080	4.946
578	4.860	5.373
579	9.560	6.671
580	4.790	5.216
581	5.240	4.556
582	5.680	5.658
583	4.990	4.883
584	6.450	5.730
585	5.130	5.546
586	4.820	5.727
587	6.230	5.444
588	5.610	6.090
589	8.800	6.683
590	4.820	6.019
591	4.840	6.158
592	4.820	6.499
593	5.940	6.605
594	5.670	4.969
595	5.540	5.828
596	5.250	6.040
597	5.840	5.441
598	6.610	5.670
599	5.740	4.853
600	5.690	6.031
601	14.020	6.841
602	5.830	6.909
603	4.870	5.109
604	5.570	6.071
605	5.840	5.531
606	5.290	6.073
607	5.810	6.404
608	7.970	6.593
609	5.870	5.892
610	5.180	4.867
611	5.270	5.897
612	5.690	4.667
613	5.620	5.545
614	5.710	4.569
615	5.790	6.315
616	4.930	5.208
617	5.320	6.180
618	4.810	5.215
619	6.840	5.496
620	5.280	4.981
621	5.310	7.006
622	5.060	6.326
623	5.360	5.706
624	4.660	5.337
625	5.360	5.715
626	5.390	6.338
627	5.160	5.813
628	7.090	7.369
629	6.430	5.697
630	4.690	5.280
631	4.660	6.822
632	5.030	5.241
633	4.860	5.265
634	6.350	5.748
635	5.730	5.940
636	5.930	5.887
637	5.760	5.254
638	5.590	6.264
639	6.140	5.942
640	8.000	6.201
641	6.080	5.808
642	5.220	6.008
643	6.340	7.274
644	4.710	5.279
645	4.720	5.345
646	5.340	6.833
647	6.210	6.252
648	9.050	6.756
649	5.340	6.693
650	4.690	4.835
651	5.210	5.996
652	4.730	6.171
653	5.170	5.066
654	5.850	5.320
655	4.930	5.617
656	4.960	7.124
657	5.090	4.928
658	5.840	4.936
659	4.620	5.496
660	5.380	5.944
661	5.560	6.185
662	5.430	5.110
663	4.750	5.886
664	5.290	5.039
665	4.940	5.343
666	5.450	5.357
667	5.610	6.464
668	6.960	6.533
669	5.800	5.823
670	5.310	5.598
671	4.990	4.833
672	6.080	6.427
673	4.440	5.473
674	5.130	6.127
675	5.420	5.800
676	5.410	5.781
677	4.910	5.108
678	4.950	6.327
679	5.090	6.236
680	5.730	5.086
681	4.920	5.448
682	4.790	4.976
683	5.780	4.652
684	20.460	5.932
685	11.230	6.100
686	4.910	6.121
687	5.210	6.171
688	5.560	5.822
689	5.080	5.128
690	5.660	4.470
691	6.020	5.689
692	4.860	5.146
693	5.000	6.447
694	5.900	5.252
695	4.780	4.450
696	5.440	7.227
697	5.380	6.687
698	5.710	5.248
699	5.800	6.717
700	5.270	4.688
701	5.500	5.218
702	4.790	5.249
703	4.790	5.245
704	5.340	6.318
705	4.750	5.107
706	4.950	4.927
707	5.610	4.559
708	4.900	4.327
709	5.950	4.833
710	5.330	5.395
711	5.150	5.273
712	4.510	4.851
713	6.160	6.124
714	6.100	6.049
715	5.040	6.701
716	4.150	5.393
717	5.080	5.586
718	5.350	4.118
719	4.850	5.994
720	4.820	5.593
721	5.870	5.671
722	6.140	7.061
723	4.910	5.884
724	7.860	6.413
725	5.450	5.119
726	6.230	6.357
727	4.430	6.205
728	5.170	4.746
729	4.860	5.263
730	5.010	6.144
731	4.990	5.956
732	6.720	5.734
733	5.590	6.577
734	4.870	6.244
735	4.900	5.934
736	5.120	6.871
737	5.360	6.318
738	4.910	5.890
739	4.770	6.008
740	6.190	5.967
741	5.180	5.076
742	5.140	4.656
743	4.600	5.037
744	4.610	7.155
745	7.700	6.974
746	5.080	5.177
747	8.670	5.605
748	6.300	6.187
749	5.630	5.756
750	5.790	5.029
751	5.640	4.967
752	4.570	5.038
753	5.580	4.810
754	6.230	4.566
755	5.450	4.560
756	5.620	5.083
757	5.280	4.958
758	4.660	5.714
759	4.060	5.325
760	5.000	5.455
761	4.790	5.210
762	5.530	6.226
763	7.480	6.907
764	5.080	6.551
765	5.310	5.371
766	5.180	4.963
767	4.960	5.544
768	5.260	5.718
769	6.920	6.249
770	5.380	6.166
771	4.590	6.225
772	4.990	5.979
773	7.600	6.680
774	5.340	5.974
775	4.900	4.884
776	5.280	5.754
777	7.800	6.441
778	8.620	5.791
779	4.770	4.475
780	5.310	6.621
781	4.910	6.020
782	8.420	6.570
783	4.760	5.685
784	4.520	5.427
785	4.640	5.554
786	4.990	6.125
787	5.140	5.107
788	5.870	6.912
789	5.650	5.954
790	5.130	5.537
791	5.790	6.325
792	4.740	6.232
793	6.160	5.859
794	5.210	7.024
795	5.250	6.182
796	5.060	5.512
797	5.840	6.160
798	4.960	5.725
799	5.400	6.637
800	5.570	6.357
801	5.180	5.681
802	5.280	5.301
803	4.800	6.070
804	5.230	4.843
805	5.140	6.415
806	5.730	5.413
807	5.220	5.431
808	5.320	6.525
809	5.170	5.590
810	4.850	5.742
811	5.380	5.948
812	5.010	5.793
813	6.290	5.278
814	5.840	5.968
815	4.800	6.280
816	8.300	5.727
817	5.140	6.165
818	6.220	6.336
819	4.830	6.188
820	4.830	5.289
821	6.100	5.603
822	5.980	7.513
823	5.760	7.097
824	5.000	5.230
825	5.660	6.355
826	5.370	4.946
827	5.890	6.242
828	5.370	5.788
829	5.930	6.344
830	5.730	5.613
831	5.360	5.030
832	7.100	6.109
833	5.300	5.205
834	5.140	5.547
835	5.610	5.223
836	4.740	5.580
837	4.750	5.115
838	5.130	5.398
839	11.610	5.683
840	5.510	6.295
841	5.620	6.292
842	5.070	5.316
843	4.770	4.444
844	8.570	6.965
845	7.090	6.072
846	7.190	7.643
847	4.650	5.180
848	5.570	6.018
849	5.910	5.827
850	6.780	5.894
851	5.270	5.342
852	4.710	4.615
853	5.880	6.058
854	5.050	5.384
855	5.620	6.144
856	4.990	6.431
857	5.180	5.651
858	5.280	6.165
859	5.950	6.371
860	5.560	5.766
861	5.560	6.070
862	5.710	6.084
863	5.670	5.509
864	4.740	5.237
865	4.670	4.996
866	5.310	5.886
867	5.430	5.110
868	5.820	5.351
869	4.460	5.508
870	5.990	5.508
871	7.350	6.340
872	4.550	4.985
873	5.490	6.017
874	4.900	5.870
875	5.420	5.315
876	5.350	6.025
877	5.100	5.394
878	5.530	4.690
879	5.630	5.319
880	5.100	5.182
881	6.360	6.344
882	6.510	6.613
883	5.260	6.538
884	4.490	5.513
885	7.630	6.086
886	5.040	5.321
887	5.820	6.093
888	5.500	5.727
889	5.410	4.580
890	5.370	6.205
891	5.480	5.894
892	9.640	5.717
893	5.300	5.785
894	5.470	5.219
895	5.390	4.478
896	5.460	6.408
897	5.830	5.755
898	5.160	6.212
899	6.140	5.053
900	5.650	5.475
901	5.480	5.164
902	5.310	5.761
903	4.720	5.949
904	6.010	6.316
905	5.110	6.508
906	5.620	5.320
907	4.750	4.688
908	5.410	5.318
909	5.520	5.650
910	4.780	6.017
911	5.460	6.002
912	5.130	5.400
913	5.900	5.759
914	12.440	6.643
915	5.690	6.044
916	6.910	6.517
917	5.380	5.789
918	4.910	5.063
919	5.290	4.917
920	5.050	4.824
921	7.230	6.520
922	4.720	5.756
923	5.060	6.522
924	5.930	6.043
925	5.620	4.383
926	5.400	5.722
927	5.190	6.468
928	6.290	8.184
929	6.200	6.181
930	8.870	6.928
931	5.620	6.433
932	5.310	6.249
933	6.450	5.892
934	6.340	5.883
935	5.570	5.918
936	5.710	4.605
937	5.430	6.303
938	17.530	7.940
939	5.210	5.541
940	5.370	6.352
941	5.460	5.548
942	5.540	5.019
943	5.660	5.160
944	5.440	5.755
945	5.750	6.017
946	5.180	4.551
947	5.190	4.909
948	5.250	4.892
949	5.590	4.535
950	5.320	5.860
951	7.670	8.078
952	6.020	6.256
953	5.200	7.146
954	5.790	6.237
955	4.880	5.718
956	4.520	5.155
957	4.880	6.065
958	4.710	6.170
959	6.980	7.138
960	5.360	6.079
961	5.410	6.650
962	5.590	5.516
963	5.410	5.353
964	4.860	5.641
965	5.330	4.854
966	5.540	4.414
967	4.910	4.711
968	5.510	6.824
969	4.760	5.531
970	6.100	6.570
971	5.260	6.888
972	5.030	6.136
973	11.110	6.939
974	5.320	5.968
975	5.260	5.998
976	5.500	6.344
977	4.900	5.521
978	5.020	5.927
979	5.130	4.740
980	6.070	6.511
981	5.100	4.728
982	5.490	5.876
983	6.010	5.421
984	4.860	6.423
985	7.250	5.932
986	5.050	5.178
987	11.390	6.003
988	6.250	5.985
989	5.020	6.110
990	5.120	5.969
991	5.020	5.379
992	5.270	5.255
993	5.940	6.241
994	6.580	6.250
995	5.400	5.110
996	5.500	6.582
997	5.330	6.915
998	5.540	4.960
999	6.360	5.759

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
0	0	41	24.96	23.10	99.59	20.23	76.88	49.60	27.28	1.82	1.31	4.43	1.37	2.65	5.87	77.25	349.39	NaN	NaN	NaN	NaN	NaN	5.34	5.21	166.1	0.479	91.9	31.9	347.0	12.8	166.0	9.9	17.4	0.164	54.1	34.2	6.5	4.7	0.6
1	0	41	24.57	36.25	67.21	79.00	79.43	47.76	31.67	1.51	2.81	4.06	0.93	2.63	5.26	87.12	486.78	NaN	NaN	NaN	NaN	NaN	7.65	5.21	156.0	0.456	87.5	29.9	342.0	13.4	277.0	9.2	10.3	0.260	52.0	36.7	5.8	4.7	0.8
2	0	46	20.82	15.23	63.69	38.17	86.23	48.00	38.23	1.26	0.99	4.13	1.64	2.01	4.77	78.19	452.07	0.010	0.020	0.010	1.370	1.070	4.60	4.76	148.8	0.438	91.9	31.3	340.0	13.0	241.0	8.3	16.6	0.199	48.1	40.3	7.7	3.2	0.8
3	1	22	14.99	10.59	74.08	20.22	70.98	44.02	26.96	1.63	1.06	6.89	1.43	5.04	4.23	61.46	368.85	NaN	NaN	NaN	NaN	NaN	9.36	4.29	137.0	0.403	93.9	31.9	340.0	12.6	252.0	10.3	10.8	0.260	41.7	46.5	6.7	4.6	0.5
4	1	48	20.07	14.78	75.79	22.72	78.05	41.83	36.22	1.15	0.97	5.37	1.27	3.65	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.07	5.15	106.0	0.354	68.7	20.6	299.0	16.6	316.0	11.1	14.0	0.350	56.6	33.1	9.1	0.6	0.6
5	1	74	23.72	22.59	81.23	23.35	76.46	45.85	30.61	1.50	2.45	6.65	1.81	4.28	6.11	66.66	383.87	0.050	0.550	0.010	1.220	1.570	7.00	4.64	141.3	0.413	88.8	30.4	343.0	13.5	249.0	8.5	17.0	0.211	42.9	47.0	7.1	2.1	1.0
6	0	31	24.97	25.53	109.03	65.42	80.82	46.40	34.42	1.35	1.51	5.60	1.33	3.82	6.14	95.98	446.10	NaN	NaN	NaN	NaN	NaN	6.81	6.03	172.0	0.504	83.6	28.5	341.0	12.0	246.0	10.8	13.3	0.270	52.9	32.0	11.3	3.1	0.7
7	0	55	37.32	40.03	88.49	25.15	74.17	41.63	32.54	1.28	1.32	5.92	1.28	4.20	6.31	92.16	344.73	NaN	NaN	NaN	NaN	NaN	5.78	4.90	158.0	0.448	91.4	32.2	353.0	12.5	282.0	10.5	13.0	0.300	52.8	36.9	6.6	2.8	0.9
8	0	39	21.70	39.17	102.91	67.09	76.12	49.31	26.81	1.84	2.07	5.50	1.51	3.60	4.98	78.57	353.31	0.060	0.100	0.030	2.230	2.170	6.96	4.94	163.0	0.482	97.6	33.0	338.0	11.8	275.0	9.8	11.4	0.270	53.1	35.9	7.3	3.3	0.4
9	1	35	18.89	15.79	78.21	25.75	75.94	46.64	29.30	1.59	1.25	3.98	1.55	2.02	6.61	86.88	266.52	0.010	0.020	0.020	2.450	2.350	8.91	4.81	144.0	0.437	90.9	29.9	330.0	11.8	247.0	10.7	12.6	0.270	65.6	27.3	5.4	1.0	0.7
10	0	47	27.33	35.76	80.26	84.97	75.84	43.71	32.13	1.36	1.96	4.32	0.90	2.84	5.60	66.49	333.60	NaN	NaN	NaN	NaN	NaN	5.44	4.97	160.0	0.452	90.9	32.2	354.0	12.4	178.0	10.8	13.5	0.190	54.0	35.7	8.1	1.3	0.9
11	0	50	22.89	28.46	77.54	48.16	73.80	48.32	25.48	1.90	3.32	4.47	1.14	2.65	4.69	74.53	493.07	NaN	NaN	NaN	NaN	NaN	6.80	4.86	151.0	0.453	93.2	31.1	333.0	11.9	259.0	10.3	12.0	0.270	57.8	33.1	7.5	1.2	0.4
12	1	48	30.11	34.70	92.67	16.39	77.86	43.05	34.81	1.24	1.21	3.69	1.43	1.94	4.39	67.54	264.32	NaN	NaN	NaN	NaN	NaN	5.26	4.89	137.0	0.407	83.2	28.0	337.0	12.0	223.0	12.4	17.2	0.280	38.8	48.5	7.2	4.2	1.3
13	1	36	16.82	11.80	67.79	13.16	76.84	47.56	29.28	1.62	0.27	2.85	1.93	0.56	3.47	73.26	345.48	NaN	NaN	NaN	NaN	NaN	6.67	4.58	141.0	0.415	90.6	30.8	340.0	11.9	330.0	9.2	9.9	0.300	72.5	20.5	6.1	0.6	0.3
14	1	37	24.58	24.64	96.43	27.79	92.31	46.98	45.33	1.04	1.22	5.20	1.21	3.55	3.46	65.99	383.20	NaN	NaN	NaN	NaN	NaN	8.87	4.69	146.0	0.428	91.3	31.1	341.0	12.6	344.0	11.9	15.5	0.410	57.5	33.4	6.8	1.5	0.8
15	1	43	20.07	14.81	72.11	33.98	74.43	44.49	29.94	1.49	1.52	5.51	1.58	3.52	5.67	62.26	300.92	NaN	NaN	NaN	NaN	NaN	10.61	4.48	136.0	0.409	91.3	30.4	333.0	12.7	424.0	10.7	12.7	0.450	64.9	28.5	5.0	1.1	0.5
16	0	77	18.49	17.29	104.91	41.33	76.02	42.39	33.63	1.26	4.59	4.05	1.02	2.49	10.68	134.63	437.37	NaN	NaN	NaN	NaN	NaN	12.84	4.95	158.0	0.442	89.3	31.9	357.0	12.0	303.0	9.9	11.5	0.300	73.0	17.5	7.2	1.9	0.4
17	0	42	24.71	30.26	95.70	37.97	81.87	43.85	38.02	1.15	3.87	5.83	1.11	4.07	6.77	90.66	485.17	NaN	NaN	NaN	NaN	NaN	8.13	6.39	121.0	0.392	61.3	18.9	309.0	18.6	263.0	11.4	16.1	0.300	55.0	35.8	8.0	1.0	0.2
18	0	39	31.44	43.97	92.58	35.84	79.74	46.49	33.25	1.40	1.33	4.55	1.11	3.40	4.75	107.33	565.31	NaN	NaN	NaN	NaN	NaN	8.88	5.34	155.0	0.466	87.3	29.0	333.0	13.2	274.0	11.2	13.4	0.310	61.5	28.2	6.0	3.5	0.8
19	0	38	23.58	31.19	102.16	44.01	81.50	51.16	30.34	1.69	2.63	5.93	1.20	4.29	3.07	94.16	468.95	NaN	NaN	NaN	NaN	NaN	6.74	5.70	171.0	0.498	87.4	30.0	343.0	13.9	193.0	11.2	13.6	0.220	54.4	36.8	7.1	1.3	0.4
20	0	30	33.25	67.19	82.22	59.91	82.80	49.32	33.48	1.47	3.44	5.50	1.18	4.02	3.79	103.15	493.38	NaN	NaN	NaN	NaN	NaN	6.44	6.12	181.0	0.540	88.2	29.6	335.0	12.1	256.0	11.6	16.2	0.300	58.4	33.7	5.3	2.0	0.6
21	0	51	26.53	20.44	67.03	25.84	75.70	48.68	27.02	1.80	1.87	4.48	1.36	2.81	4.25	98.69	398.20	NaN	NaN	NaN	NaN	NaN	6.46	5.25	175.2	0.509	97.0	33.4	344.0	12.9	166.0	9.9	17.2	0.164	58.2	33.9	5.2	2.4	0.3
22	1	38	24.13	34.02	77.32	18.78	76.12	44.87	31.25	1.44	1.42	4.78	1.73	2.71	3.56	65.55	236.15	NaN	NaN	NaN	NaN	NaN	5.98	3.31	81.0	0.264	79.8	24.5	307.0	12.8	571.0	10.1	10.1	0.580	55.9	27.6	11.5	4.3	0.7
23	1	33	15.77	11.43	95.86	20.45	83.11	47.37	35.74	1.33	0.94	3.49	1.22	1.97	5.93	65.04	254.75	NaN	NaN	NaN	NaN	NaN	5.48	4.68	140.0	0.412	88.0	29.9	340.0	12.1	196.0	11.5	14.2	0.230	45.8	47.4	5.3	1.1	0.4
24	1	28	20.24	12.55	61.90	7.99	76.97	47.89	29.08	1.65	0.60	4.88	1.92	2.45	4.27	71.37	241.98	NaN	NaN	NaN	NaN	NaN	4.90	4.86	147.0	0.440	90.5	30.2	334.0	12.7	206.0	10.4	11.5	0.210	49.0	44.1	5.5	0.8	0.6
25	1	34	20.89	16.16	102.40	14.69	77.07	43.72	33.35	1.31	1.40	5.93	1.47	4.37	4.36	68.11	263.35	NaN	NaN	NaN	NaN	NaN	7.78	4.90	139.0	0.418	85.3	28.4	333.0	13.9	293.0	11.8	15.4	0.350	55.4	39.2	4.5	0.3	0.6
26	0	34	32.03	52.74	102.73	34.53	79.11	50.40	28.71	1.76	1.18	5.93	1.68	4.13	6.20	80.23	446.16	NaN	NaN	NaN	NaN	NaN	6.33	5.17	153.0	0.458	88.6	29.6	334.0	12.0	264.0	9.4	10.0	0.250	56.1	35.4	6.8	1.1	0.6
27	0	38	34.62	54.43	119.68	51.14	78.47	49.26	29.21	1.69	2.46	5.37	1.12	3.92	4.00	88.32	336.44	NaN	NaN	NaN	NaN	NaN	7.26	5.58	172.0	0.506	90.7	30.8	340.0	12.2	223.0	11.1	15.1	0.250	55.1	33.1	9.1	1.9	0.8
28	0	60	28.36	32.30	64.43	30.76	71.38	43.21	28.17	1.53	0.92	4.59	1.53	2.69	8.25	78.72	322.39	NaN	NaN	NaN	NaN	NaN	4.83	5.04	156.0	0.468	92.9	31.0	333.0	12.3	201.0	10.5	11.4	0.210	54.2	35.8	7.5	1.7	0.8
29	0	55	29.60	34.07	99.96	41.95	82.33	45.49	36.84	1.23	1.62	5.28	1.25	3.58	5.33	88.48	389.35	NaN	NaN	NaN	NaN	NaN	5.85	5.40	165.0	0.493	91.3	30.6	335.0	13.2	260.0	11.1	13.5	0.290	55.7	30.1	10.4	2.6	1.2
30	0	56	22.68	16.93	102.98	30.43	73.75	47.42	26.33	1.80	0.69	5.41	2.07	2.85	3.66	68.16	245.80	NaN	NaN	NaN	NaN	NaN	7.78	4.71	150.0	0.449	95.3	31.8	334.0	12.6	308.0	10.4	12.1	0.320	50.4	38.0	9.4	1.8	0.4
31	0	59	39.70	28.17	118.78	22.64	76.72	43.77	32.95	1.33	0.78	5.43	1.73	3.43	5.99	96.64	278.50	NaN	NaN	NaN	NaN	NaN	6.20	5.60	166.0	0.482	86.1	29.6	344.0	13.1	187.0	10.2	12.1	0.190	50.3	42.1	6.8	0.5	0.3
32	0	58	20.84	23.66	83.13	150.09	71.55	44.96	26.59	1.69	4.85	6.78	1.77	4.50	7.52	96.31	323.78	NaN	NaN	NaN	NaN	NaN	9.89	4.60	152.0	0.432	93.9	33.0	352.0	12.4	273.0	9.8	10.6	0.270	39.1	53.0	5.9	1.3	0.7
33	0	32	30.23	39.73	67.52	27.26	79.21	46.96	32.25	1.46	2.10	6.06	1.35	4.10	4.35	73.93	389.17	NaN	NaN	NaN	NaN	NaN	4.96	5.41	161.0	0.473	87.4	29.8	340.0	12.1	253.0	10.8	13.5	0.270	56.8	33.1	8.1	1.4	0.6
34	0	35	20.93	37.67	64.63	46.95	75.52	46.23	29.29	1.58	3.51	4.77	0.88	3.29	4.09	100.13	378.25	NaN	NaN	NaN	NaN	NaN	6.71	5.08	158.0	0.463	91.1	31.1	341.0	12.3	210.0	10.4	12.6	0.220	34.1	52.5	7.9	4.9	0.6
35	1	38	18.81	12.55	52.55	16.03	81.27	48.17	33.10	1.46	1.02	5.30	1.46	3.48	8.48	77.19	366.38	NaN	NaN	NaN	NaN	NaN	6.93	5.04	145.0	0.430	85.3	28.8	337.0	13.0	272.0	10.1	11.7	0.270	55.4	35.1	6.9	2.2	0.4
36	1	47	26.58	16.03	71.79	30.68	82.17	42.87	39.30	1.09	1.43	5.76	1.24	4.02	3.48	63.55	243.69	NaN	NaN	NaN	NaN	NaN	7.36	4.53	134.9	0.396	87.3	29.8	341.0	13.3	335.0	8.5	15.7	0.283	51.6	35.0	8.0	4.6	0.8
37	1	36	17.99	10.11	55.82	26.14	75.47	43.18	32.29	1.34	1.53	5.70	1.27	3.92	6.48	69.54	311.17	NaN	NaN	NaN	NaN	NaN	7.48	4.02	129.0	0.375	93.3	32.1	344.0	12.3	274.0	10.0	11.1	0.270	57.6	35.0	6.0	0.9	0.5
38	1	45	20.58	11.25	60.21	11.62	75.65	43.98	31.67	1.39	0.94	4.63	1.65	2.75	5.38	73.60	227.93	NaN	NaN	NaN	NaN	NaN	6.12	4.51	135.6	0.405	89.8	30.0	335.0	12.7	213.0	8.7	16.5	0.185	50.0	40.9	7.9	0.6	0.5
39	1	53	20.47	10.76	132.27	21.11	76.45	46.28	30.17	1.53	1.47	6.65	1.66	4.37	3.06	65.44	225.15	NaN	NaN	NaN	NaN	NaN	5.42	4.46	134.0	0.408	91.5	30.0	328.0	13.1	402.0	9.7	10.5	0.390	47.8	43.2	7.0	1.3	0.7

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
2	0	46	20.82	15.23	63.69	38.17	86.23	48.00	38.23	1.26	0.99	4.13	1.64	2.01	4.77	78.19	452.07	0.010	0.020	0.010	1.370	1.070	4.60	4.76	148.8	0.438	91.9	31.3	340.0	13.0	241.0	8.3	16.6	0.199	48.1	40.3	7.7	3.2	0.8
5	1	74	23.72	22.59	81.23	23.35	76.46	45.85	30.61	1.50	2.45	6.65	1.81	4.28	6.11	66.66	383.87	0.050	0.550	0.010	1.220	1.570	7.00	4.64	141.3	0.413	88.8	30.4	343.0	13.5	249.0	8.5	17.0	0.211	42.9	47.0	7.1	2.1	1.0
8	0	39	21.70	39.17	102.91	67.09	76.12	49.31	26.81	1.84	2.07	5.50	1.51	3.60	4.98	78.57	353.31	0.060	0.100	0.030	2.230	2.170	6.96	4.94	163.0	0.482	97.6	33.0	338.0	11.8	275.0	9.8	11.4	0.270	53.1	35.9	7.3	3.3	0.4
9	1	35	18.89	15.79	78.21	25.75	75.94	46.64	29.30	1.59	1.25	3.98	1.55	2.02	6.61	86.88	266.52	0.010	0.020	0.020	2.450	2.350	8.91	4.81	144.0	0.437	90.9	29.9	330.0	11.8	247.0	10.7	12.6	0.270	65.6	27.3	5.4	1.0	0.7
165	1	50	21.20	13.25	59.69	17.04	77.54	42.98	34.56	1.24	0.53	4.92	1.40	3.16	6.19	70.22	366.27	0.060	0.120	0.060	1.410	1.780	6.84	4.51	147.0	0.414	91.8	32.6	355.0	11.4	301.0	9.7	10.8	0.290	57.9	29.7	10.8	1.0	0.6
166	1	45	19.24	21.73	82.89	24.31	77.84	45.73	32.11	1.42	2.91	6.79	1.30	4.78	4.20	65.02	384.53	0.040	0.120	0.030	4.030	1.480	5.78	4.14	135.0	0.400	96.6	32.6	338.0	13.0	334.0	9.7	11.0	0.320	57.9	29.2	8.1	3.8	1.0
188	0	39	22.89	23.97	82.17	19.52	73.38	42.71	30.67	1.39	1.49	6.39	1.19	4.77	4.86	78.53	372.79	0.010	10.910	0.010	1.670	1.110	8.56	5.37	157.0	0.483	89.9	29.2	325.0	12.8	193.0	10.7	14.0	0.210	57.2	35.0	6.4	1.2	0.2
289	1	39	21.96	18.43	57.86	16.06	72.29	43.63	28.66	1.52	0.46	3.57	1.33	1.97	4.21	68.26	231.81	0.030	11.990	0.050	1.010	1.720	5.86	4.18	122.0	0.370	88.5	29.2	330.0	12.6	239.0	12.0	15.9	0.290	54.1	35.5	4.8	4.6	1.0
380	1	65	21.68	19.23	99.43	24.31	72.26	40.99	31.27	1.31	0.95	4.69	1.44	3.03	5.24	79.35	287.47	0.050	1.950	0.010	3.480	1.480	6.35	4.03	127.0	0.384	95.3	31.5	331.0	12.1	244.0	11.5	13.6	0.280	55.3	33.2	8.3	2.4	0.8
394	0	24	22.97	16.51	64.32	25.25	77.55	50.75	26.80	1.89	1.26	5.23	1.38	3.33	6.58	82.87	464.58	0.010	1.420	0.010	1.650	1.830	8.85	6.55	189.0	0.570	87.0	28.9	332.0	13.4	287.0	10.6	13.0	0.300	52.0	39.7	6.6	1.4	0.3
395	1	30	19.20	9.66	64.32	13.94	80.87	48.14	32.73	1.47	0.74	4.59	1.39	2.72	3.35	60.47	189.74	0.050	16.220	0.060	2.320	1.750	6.34	5.18	152.0	0.460	88.8	29.3	330.0	13.0	255.0	10.4	12.7	0.270	58.5	32.8	6.3	1.6	0.8
916	1	32	25.85	34.18	67.93	47.55	81.88	48.19	33.69	1.43	3.15	3.87	1.07	2.38	1.74	60.09	330.84	0.050	1.740	0.030	2.430	1.530	9.70	5.07	143.0	0.439	86.6	28.2	326.0	12.0	410.0	10.4	11.9	0.430	59.2	31.1	6.7	2.1	0.9
1116	1	71	21.68	11.68	116.08	19.18	81.85	46.90	34.95	1.34	1.97	6.16	1.63	4.06	3.75	66.88	348.52	0.080	0.960	0.010	2.080	2.040	5.05	4.63	129.0	0.392	84.7	27.9	329.0	12.3	277.0	12.3	15.5	0.340	60.2	32.1	6.3	0.8	0.6
1117	0	60	24.01	26.84	91.84	52.39	74.88	46.15	28.73	1.61	2.26	4.74	1.10	3.03	8.69	85.02	420.13	0.010	1.300	0.020	0.610	0.130	8.44	4.91	150.0	0.449	91.4	30.5	334.0	12.2	325.0	10.2	11.5	0.330	59.2	29.6	7.7	2.6	0.9
1118	0	54	18.84	13.62	120.38	18.51	73.35	40.65	32.70	1.24	1.28	4.94	1.07	3.37	5.14	85.12	388.48	0.060	19.410	0.250	1.160	1.920	3.17	4.79	150.0	0.434	90.6	31.3	346.0	12.6	166.0	10.8	12.1	0.180	46.5	41.6	8.2	2.8	0.9
1566	1	77	12.54	6.78	88.10	16.43	82.56	42.79	39.77	1.08	2.06	5.51	1.21	3.89	3.80	69.37	345.12	0.120	0.130	0.110	1.620	5.400	8.56	5.22	140.0	0.437	83.7	26.8	320.0	13.4	254.0	12.3	17.1	0.310	72.5	21.5	4.3	1.2	0.5
2242	0	68	23.62	24.03	83.69	28.47	73.29	45.35	27.94	1.62	0.99	6.04	1.38	4.15	4.77	83.98	377.04	0.070	7.720	0.110	1.160	0.790	6.43	4.95	150.0	0.449	90.7	30.3	334.0	12.7	184.0	10.8	12.6	0.200	58.8	30.9	8.1	1.1	1.1
2243	1	64	27.68	16.24	96.80	15.58	81.84	46.80	35.04	1.34	0.62	4.93	1.93	2.46	4.98	75.17	345.99	30.540	0.950	0.010	0.640	0.130	5.23	4.44	137.0	0.412	92.8	30.9	333.0	13.2	150.0	10.9	13.1	0.160	65.0	30.0	4.0	0.4	0.6
2244	1	49	18.75	12.99	59.28	13.75	75.43	47.24	28.19	1.68	0.83	3.60	1.13	2.29	3.72	67.21	286.75	0.060	0.010	0.010	1.340	0.420	7.14	5.46	158.0	0.472	86.4	28.9	335.0	12.6	215.0	11.2	14.5	0.240	62.6	29.4	6.7	0.7	0.6
2245	0	79	33.28	25.03	46.33	30.94	77.17	44.40	32.77	1.35	0.81	5.49	1.91	3.12	4.91	86.50	292.39	0.080	0.340	0.110	1.610	2.780	7.37	4.77	154.0	0.447	93.7	32.3	345.0	12.6	201.0	11.5	15.1	0.230	46.6	44.0	7.1	1.9	0.4
2246	1	49	30.81	18.36	94.00	21.41	76.39	46.96	29.43	1.60	2.74	6.11	1.64	3.79	5.16	65.88	372.77	0.070	7.600	0.010	1.020	2.060	5.12	4.50	146.0	0.439	97.6	32.4	333.0	12.6	153.0	9.0	9.5	0.140	50.1	39.3	9.4	0.6	0.6
2247	1	34	23.59	16.11	72.82	17.95	78.45	45.60	32.85	1.39	1.40	5.71	1.62	3.65	4.05	73.29	321.32	0.050	42.480	0.070	0.860	2.370	7.60	4.60	133.0	0.417	90.7	28.9	319.0	13.5	387.0	10.7	12.6	0.410	46.6	42.6	6.6	2.9	1.3
2248	1	45	19.19	15.10	83.81	14.60	77.29	45.97	31.32	1.47	1.20	5.05	1.56	3.19	4.35	58.82	185.53	0.090	0.110	0.010	1.490	0.710	5.48	4.68	142.0	0.419	89.5	30.3	339.0	13.1	191.0	12.3	15.9	0.230	64.8	27.4	6.0	1.1	0.7
2249	0	25	24.37	20.54	71.65	23.53	78.14	47.04	31.10	1.51	0.99	5.45	1.51	3.30	4.94	85.87	389.44	0.130	27.850	0.140	1.550	0.940	6.44	5.37	163.0	0.486	90.5	30.4	335.0	11.6	197.0	11.2	14.6	0.220	52.8	31.7	9.0	5.7	0.8
2250	1	51	23.23	28.57	90.00	51.39	82.73	46.42	36.31	1.28	1.77	6.76	1.49	4.67	5.45	68.25	464.85	0.160	2.030	0.120	0.920	2.060	4.61	4.44	137.0	0.418	94.1	30.9	328.0	12.1	247.0	10.8	12.9	0.270	52.7	39.0	7.4	0.7	0.2
2252	0	35	78.07	122.31	100.73	206.33	80.56	49.32	31.24	1.58	2.90	5.61	1.05	4.21	5.41	80.21	581.72	0.070	11.500	0.090	1.140	1.620	6.84	5.70	171.0	0.504	88.4	30.0	339.0	12.1	321.0	10.3	13.2	0.330	53.8	39.0	5.4	1.5	0.3
2253	1	61	22.04	23.11	96.81	28.43	81.67	46.53	35.14	1.32	1.78	7.17	1.82	4.92	4.78	57.56	303.74	0.040	24.700	0.010	1.230	2.260	7.28	5.03	145.0	0.430	85.5	28.8	337.0	14.2	273.0	11.3	13.8	0.310	45.7	44.8	6.9	1.6	1.0
2254	1	65	28.82	29.46	106.55	14.00	75.74	43.63	32.11	1.36	1.52	3.58	1.07	2.25	4.43	65.74	351.55	0.150	0.070	0.150	1.390	1.280	5.27	4.87	142.0	0.438	89.9	29.2	324.0	12.7	218.0	11.5	13.6	0.250	59.4	30.6	8.9	0.9	0.2
2255	0	50	32.17	50.27	115.34	71.97	79.42	46.49	32.93	1.41	1.79	5.19	1.07	3.71	3.42	73.91	718.91	0.010	4.750	0.030	0.510	0.350	6.55	5.27	165.0	0.478	90.7	31.3	345.0	12.6	322.0	10.7	13.5	0.350	58.6	29.9	5.3	4.7	1.5
2256	1	53	28.22	32.94	73.73	29.34	75.54	48.84	26.70	1.83	1.78	5.95	1.53	3.87	8.29	63.22	326.12	0.090	7.430	0.080	0.700	3.140	7.15	4.92	144.0	0.446	90.7	29.3	323.0	14.0	202.0	13.0	17.5	0.260	51.6	39.7	7.3	0.8	0.6
2257	0	45	14.64	12.40	86.86	20.48	66.56	42.06	24.50	1.72	1.71	5.81	1.21	4.18	4.44	85.87	465.66	0.040	2.010	0.100	0.630	2.680	6.58	6.03	175.0	0.511	84.7	29.0	342.0	11.9	238.0	10.6	13.4	0.250	42.5	47.6	6.8	2.3	0.8
2258	0	56	12.68	9.10	56.72	21.16	72.93	43.84	29.09	1.51	0.59	4.31	1.39	2.56	6.44	82.09	388.61	0.130	0.500	0.160	1.670	3.000	6.04	4.21	132.0	0.373	88.6	31.4	354.0	12.8	365.0	9.9	11.5	0.360	73.5	18.2	5.6	2.0	0.7
2259	0	59	22.41	25.16	104.78	43.63	76.38	45.23	31.15	1.45	2.20	4.80	0.93	3.45	5.10	75.17	389.26	0.080	10.430	0.140	1.900	2.200	7.06	5.94	168.0	0.514	86.5	28.3	327.0	12.3	328.0	9.3	10.6	0.310	45.5	45.2	6.5	2.0	0.8
2260	1	50	24.04	18.35	89.03	13.85	77.12	42.10	35.02	1.20	1.49	4.08	1.04	2.59	7.95	61.33	198.57	39.880	0.080	0.080	0.400	0.010	5.53	4.57	134.0	0.403	88.2	29.3	333.0	12.4	224.0	11.4	14.3	0.260	49.2	42.0	5.4	2.5	0.9
2266	0	59	21.83	18.27	134.45	37.15	71.48	42.29	29.19	1.45	0.87	5.70	1.45	3.94	5.92	84.51	353.24	0.060	16.420	0.070	1.280	1.670	7.28	5.45	162.0	0.492	90.3	29.7	329.0	12.7	285.0	10.3	12.4	0.290	62.4	30.5	5.4	1.0	0.7
2270	1	39	17.89	10.37	57.30	12.81	74.91	43.27	31.64	1.37	1.57	5.64	1.79	3.59	4.58	56.93	330.77	0.130	3.080	0.120	1.520	2.220	6.02	4.94	139.0	0.416	84.2	28.1	334.0	13.4	206.0	10.7	12.3	0.220	55.4	38.9	4.3	0.7	0.7
2271	1	39	21.32	19.81	76.03	21.36	80.12	45.97	34.15	1.35	2.27	4.87	1.18	3.31	4.98	61.33	312.86	0.070	0.160	0.120	1.690	1.480	5.79	4.67	123.0	0.386	82.7	26.3	319.0	13.4	263.0	11.5	14.6	0.300	55.6	35.8	5.5	2.1	1.0
2274	0	50	21.50	22.46	80.69	24.49	84.11	50.36	33.75	1.49	1.14	4.88	1.42	3.11	7.44	91.53	309.93	0.170	20.560	0.100	1.550	1.380	6.20	5.48	169.0	0.506	92.3	30.8	334.0	13.4	127.0	13.5	20.2	0.170	63.9	29.7	4.8	1.0	0.6
2277	0	67	19.53	29.76	94.45	59.76	71.71	44.80	26.91	1.66	1.71	5.48	1.30	3.72	4.55	81.46	332.52	0.020	0.040	0.190	1.270	1.760	5.86	5.35	163.0	0.476	89.0	30.5	342.0	12.9	217.0	10.0	10.7	0.220	66.0	25.3	6.7	1.5	0.5
2281	0	46	24.48	30.64	87.45	34.29	77.08	45.67	31.41	1.45	1.15	5.33	1.44	3.58	4.13	85.18	322.33	0.010	0.240	0.010	1.210	0.280	6.31	5.39	174.0	0.486	90.2	32.3	358.0	12.5	271.0	10.6	12.5	0.290	65.6	27.4	5.5	1.0	0.5

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
0	0	54	23.850	26.690	116.080	34.360	82.750	46.030	36.720	1.250	1.070	5.030	1.440	3.010	4.580	65.870	303.370	0.789	5.452	0.063	1.670	1.843	9.390	4.940	151.000	0.453	91.700	30.600	333.000	12.300	241.000	10.800	12.800	0.260	58.400	33.200	7.500	0.600	0.300
1	0	50	29.750	34.980	90.070	111.430	71.900	44.090	27.810	1.590	2.480	5.510	1.220	3.620	5.540	89.990	368.760	0.789	5.452	0.063	1.670	1.843	7.580	5.840	171.000	0.518	88.700	29.300	330.000	12.000	242.000	11.500	14.600	0.280	59.300	29.300	7.700	3.200	0.500
2	0	27	23.865	25.805	93.010	67.420	75.030	44.765	30.265	1.490	4.210	6.155	1.300	3.745	5.150	73.290	383.920	0.789	5.452	0.063	1.670	1.843	9.060	5.720	171.000	0.506	88.500	29.900	338.000	12.100	398.000	8.900	9.900	0.350	50.200	40.100	7.900	1.200	0.600
3	1	53	17.980	16.630	95.950	23.410	78.160	45.440	32.720	1.390	5.940	6.800	1.380	3.870	3.920	70.820	322.090	0.030	17.100	0.100	2.840	1.810	4.600	4.610	138.000	0.420	91.100	29.900	329.000	12.600	247.000	11.600	13.900	0.290	53.700	38.000	7.200	0.700	0.400
4	1	43	19.120	19.800	76.970	15.700	80.760	46.900	33.860	1.390	0.740	6.230	2.250	3.450	6.030	73.910	377.390	0.023	11.430	0.070	3.267	1.687	6.240	4.620	147.000	0.440	95.200	31.800	334.000	12.200	335.000	10.400	11.900	0.350	52.000	39.400	8.000	0.300	0.300
5	0	48	25.770	17.660	92.060	26.010	72.290	45.450	26.840	1.690	0.840	5.110	1.790	2.750	5.130	84.760	275.420	0.017	5.760	0.040	3.693	1.563	4.970	4.880	158.000	0.479	98.200	32.400	330.000	13.200	217.000	11.400	14.900	0.250	56.800	34.200	8.000	0.600	0.400
6	1	49	51.540	40.530	67.240	23.230	74.920	45.600	29.320	1.560	0.660	6.890	1.270	5.050	2.670	66.400	415.420	0.010	0.090	0.010	4.120	1.440	4.520	5.150	133.000	0.406	78.800	25.800	328.000	14.400	490.000	9.500	10.100	0.470	58.600	32.300	7.100	1.300	0.700
7	1	51	26.510	24.770	87.710	26.600	70.310	44.390	25.920	1.710	1.200	4.930	1.330	3.280	3.750	74.170	329.150	0.040	0.080	0.180	3.090	2.010	6.540	4.970	151.000	0.440	88.500	30.400	343.000	12.900	241.000	11.000	13.400	0.270	55.400	34.900	8.000	1.100	0.600
8	1	68	28.440	22.990	85.910	38.570	79.670	44.540	35.130	1.270	1.360	6.930	1.940	4.410	4.910	62.160	426.630	0.010	0.236	0.010	2.670	1.810	8.670	4.500	142.000	0.427	94.900	31.600	333.000	12.100	369.000	10.100	11.800	0.370	55.200	35.600	7.000	1.000	1.200
9	1	64	35.650	29.050	114.980	202.290	78.080	44.580	33.500	1.330	2.030	6.720	1.390	4.750	6.320	81.930	400.490	0.040	0.010	0.040	1.920	2.150	5.950	5.240	151.000	0.467	89.100	28.800	323.000	12.900	199.000	9.300	10.400	0.190	54.300	38.800	5.000	1.200	0.700
10	1	27	23.520	18.560	67.090	12.310	78.510	43.720	34.790	1.260	0.690	4.530	1.260	2.620	3.020	59.070	198.100	0.043	0.307	0.041	1.954	2.089	8.080	4.130	120.000	0.368	89.100	29.100	326.000	13.500	364.000	10.700	12.700	0.390	64.600	24.900	8.200	1.700	0.600
11	0	71	25.160	23.370	55.890	20.080	68.600	41.980	26.620	1.580	0.720	4.570	1.500	2.670	6.790	76.750	344.010	0.046	0.604	0.043	1.989	2.027	5.250	4.820	146.000	0.439	91.100	30.300	333.000	13.000	186.000	10.500	11.700	0.190	54.600	33.000	11.200	0.800	0.400
12	0	69	29.620	32.500	115.890	18.320	65.770	42.800	22.970	1.860	0.920	4.540	1.180	3.180	5.250	95.080	363.730	0.049	0.901	0.044	2.023	1.966	5.350	5.760	173.000	0.509	88.400	30.000	340.000	13.700	219.000	11.100	14.000	0.240	60.100	29.500	6.700	3.000	0.700
13	1	69	23.280	22.285	96.305	21.790	73.025	44.205	28.820	1.590	0.940	4.150	1.640	2.240	7.645	117.315	488.290	0.051	1.199	0.046	2.057	1.904	8.400	4.540	124.000	0.397	87.400	27.300	312.000	13.600	252.000	11.300	13.900	0.290	62.100	27.900	7.900	1.100	1.000
14	0	63	16.940	12.070	76.720	25.260	80.280	45.610	34.670	1.320	2.290	5.650	1.340	3.840	10.040	139.550	612.850	0.054	1.496	0.047	2.091	1.843	8.420	4.460	135.000	0.403	90.400	30.300	335.000	12.500	275.000	11.200	13.500	0.310	52.200	40.100	5.700	1.300	0.700
15	1	35	17.330	11.070	60.560	10.920	74.690	44.370	30.320	1.460	0.570	3.860	1.390	2.070	4.760	72.860	226.950	0.057	1.793	0.049	2.126	1.781	4.810	4.820	116.000	0.383	79.500	24.100	303.000	23.100	274.000	11.100	13.300	0.300	56.500	35.600	5.200	2.300	0.400
16	1	47	19.250	26.830	83.270	42.600	76.000	47.770	28.230	1.690	1.200	5.340	1.460	3.350	5.360	75.760	323.580	0.060	2.090	0.050	2.160	1.720	7.030	4.300	135.000	0.413	96.000	31.400	327.000	12.500	295.000	10.200	11.300	0.300	57.000	32.600	6.400	3.600	0.400
17	1	80	22.750	16.700	93.240	14.730	70.180	41.950	28.230	1.490	1.910	5.020	1.210	3.250	4.960	72.050	238.960	0.075	1.055	0.045	2.700	1.665	6.100	4.680	143.000	0.439	93.800	30.600	326.000	12.500	185.000	9.800	11.100	0.180	54.900	35.600	6.700	2.000	0.800
18	1	45	21.780	12.000	44.530	14.800	71.020	43.820	27.200	1.610	1.030	5.200	1.590	3.200	5.210	85.470	246.990	0.090	0.020	0.040	3.240	1.610	5.620	4.360	122.000	0.375	86.000	28.000	325.000	12.900	340.000	9.400	10.200	0.320	61.000	32.900	3.600	2.100	0.400
19	0	68	97.900	55.230	96.230	186.950	78.000	41.420	36.580	1.130	0.560	3.400	1.960	1.010	5.400	85.040	478.470	0.084	2.621	0.041	3.094	1.608	2.800	4.860	161.000	0.485	99.800	33.100	332.000	13.400	132.000	10.300	10.900	0.140	54.300	28.600	9.600	6.800	0.700
20	0	27	25.010	50.320	90.550	44.710	76.630	45.860	30.770	1.490	1.190	5.130	1.310	3.120	3.820	89.000	471.150	0.079	5.222	0.042	2.949	1.606	17.930	5.750	173.000	0.504	87.700	30.100	343.000	12.800	392.000	9.700	11.000	0.380	55.500	36.100	6.500	1.200	0.700
21	1	51	23.310	38.425	97.980	54.085	74.375	45.280	29.095	1.560	1.340	6.350	1.550	4.290	4.740	97.830	441.355	0.073	7.823	0.043	2.803	1.603	6.240	4.240	130.000	0.378	89.200	30.700	344.000	12.700	277.000	10.300	12.100	0.290	49.400	41.700	5.300	2.600	1.000
22	0	67	21.610	26.530	105.410	63.460	72.120	44.700	27.420	1.630	2.810	6.470	1.470	4.150	5.660	106.660	411.560	0.068	10.424	0.044	2.658	1.601	6.590	5.290	160.000	0.486	91.900	30.200	329.000	12.100	252.000	10.900	13.700	0.280	66.700	24.100	5.000	3.300	0.900
23	1	62	23.300	19.390	99.780	30.930	78.910	45.860	33.050	1.390	1.900	7.750	1.740	5.200	4.540	57.920	290.180	0.062	13.026	0.046	2.512	1.599	7.880	4.740	136.000	0.413	87.100	28.700	329.000	12.200	295.000	11.000	13.500	0.330	67.400	23.600	5.500	3.000	0.500
24	0	55	24.860	14.990	60.590	53.220	73.140	42.070	31.070	1.350	1.050	4.610	1.530	2.680	3.910	86.900	549.290	0.057	15.627	0.047	2.367	1.597	7.530	5.700	164.000	0.480	84.200	28.800	342.000	12.700	233.000	10.300	11.400	0.240	72.300	20.600	5.300	1.300	0.500
25	1	48	24.010	30.010	76.120	13.700	74.430	42.420	32.010	1.330	0.830	4.630	1.280	2.910	4.130	65.870	253.850	0.051	18.228	0.048	2.221	1.594	6.880	4.620	139.000	0.408	88.300	30.100	341.000	11.600	294.000	10.100	11.400	0.300	54.000	38.700	6.000	1.000	0.300
26	1	35	18.950	12.760	89.720	13.660	81.140	46.800	34.340	1.360	1.250	4.050	1.310	2.180	5.010	72.670	215.150	0.046	20.829	0.049	2.076	1.592	6.670	5.075	152.000	0.458	90.100	29.950	333.000	11.450	291.500	10.150	11.800	0.300	57.700	33.800	7.000	1.250	0.250
27	0	47	25.510	56.960	76.740	22.930	77.530	45.160	32.370	1.400	0.640	3.690	1.160	2.220	5.170	101.530	436.270	0.040	23.430	0.050	1.930	1.590	6.460	5.530	165.000	0.508	91.900	29.800	325.000	11.300	289.000	10.200	12.200	0.300	61.400	28.900	8.000	1.500	0.200
28	0	38	35.180	56.640	106.030	48.470	82.020	48.090	33.930	1.420	1.700	5.970	1.390	3.950	4.170	81.930	417.420	0.039	23.056	0.050	1.926	1.591	6.405	5.120	155.000	0.465	90.650	30.300	334.500	11.750	261.500	11.150	14.000	0.290	57.000	32.550	8.100	2.100	0.250
29	0	46	28.340	27.870	142.430	35.440	73.690	47.900	25.790	1.860	0.970	5.490	1.610	3.420	6.570	96.060	574.960	0.039	22.681	0.049	1.921	1.591	6.350	4.710	145.000	0.421	89.400	30.800	344.000	12.200	234.000	12.100	15.800	0.280	52.600	36.200	8.200	2.700	0.300
30	1	39	33.510	46.290	73.070	15.240	71.380	43.890	27.490	1.600	0.720	6.120	1.750	4.160	5.610	64.190	267.380	0.038	22.307	0.049	1.917	1.592	4.890	4.530	142.000	0.420	92.700	31.300	338.000	12.400	193.000	10.800	12.600	0.210	54.800	36.200	8.200	0.400	0.400
31	1	44	15.820	17.800	67.810	30.980	77.300	44.110	33.190	1.330	1.220	6.860	1.590	4.800	4.760	71.630	258.500	0.038	21.933	0.049	1.913	1.592	6.830	4.400	131.000	0.387	88.000	29.800	339.000	12.800	282.000	11.100	13.600	0.310	64.700	26.200	6.000	2.500	0.600
32	0	54	36.270	23.590	67.660	37.080	72.030	45.080	26.950	1.670	1.140	4.350	1.170	3.010	4.580	83.190	428.380	0.037	21.558	0.048	1.908	1.593	5.200	5.250	156.000	0.471	89.700	29.700	331.000	12.200	210.000	11.300	14.300	0.240	47.100	43.700	7.300	1.500	0.400
33	0	54	38.270	57.040	110.070	76.150	79.150	45.290	33.860	1.340	6.120	5.790	1.050	3.640	4.230	72.110	326.190	0.037	21.184	0.048	1.904	1.593	8.110	5.110	162.000	0.458	89.600	31.700	354.000	12.100	233.000	12.000	15.600	0.280	52.100	36.000	7.500	3.500	0.900
34	0	53	22.290	18.110	77.030	25.220	76.640	45.330	31.310	1.450	0.840	5.020	1.500	3.270	6.340	75.590	351.430	0.036	20.809	0.047	1.899	1.594	6.520	4.870	158.000	0.462	94.900	32.400	342.000	12.100	230.000	11.900	15.700	0.270	53.300	36.500	7.400	2.300	0.500
35	1	33	19.450	35.040	66.120	20.940	77.400	44.960	32.440	1.390	0.870	4.240	1.710	2.350	3.230	67.240	255.210	0.036	20.435	0.047	1.895	1.594	4.800	4.150	126.000	0.375	90.400	30.400	336.000	13.000	200.000	11.200	13.700	0.220	49.200	39.800	7.500	2.700	0.800
36	1	48	26.550	20.000	121.730	22.300	81.800	45.400	36.400	1.250	1.400	5.160	1.240	3.740	3.950	61.920	381.160	0.035	20.061	0.047	1.891	1.595	8.590	4.850	146.000	0.429	88.500	30.100	340.000	12.700	301.000	9.800	11.000	0.290	51.600	42.500	5.400	0.200	0.300
37	1	43	21.980	13.340	63.460	22.550	74.480	43.480	31.000	1.400	2.000	7.100	1.680	4.690	2.850	54.500	240.730	0.035	19.686	0.046	1.886	1.595	6.240	4.130	138.000	0.395	95.600	33.400	349.000	13.100	277.000	8.800	9.100	0.240	52.600	39.700	5.300	2.100	0.300
38	1	55	25.870	17.710	102.890	16.330	74.690	44.660	30.030	1.490	1.210	6.940	1.590	4.870	6.770	66.880	311.290	0.034	19.312	0.046	1.882	1.596	4.910	4.510	144.000	0.423	93.800	31.900	340.000	12.900	170.000	12.400	16.800	0.210	53.600	37.300	7.100	1.400	0.600
39	1	55	28.690	23.070	85.480	26.230	83.480	48.850	34.630	1.410	1.650	6.030	1.490	4.050	5.440	74.930	474.520	0.033	18.938	0.046	1.878	1.597	9.400	4.640	142.000	0.429	92.500	30.600	331.000	12.700	308.000	10.200	11.300	0.310	39.100	52.300	6.300	1.300	1.000